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a/1-Introduction/1-intro-to-ML/README.md +++ /dev/null @@ -1,145 +0,0 @@ -# Introduction to machine learning - -## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/) - ---- - -[![ML for beginners - Introduction to Machine Learning for Beginners](https://img.youtube.com/vi/6mSx_KJxcHI/0.jpg)](https://youtu.be/6mSx_KJxcHI "ML for beginners - Introduction to Machine Learning for Beginners") - -> 🎥 Click the image above for a short video working through this lesson. - -Welcome to this course on classical machine learning for beginners! Whether you're completely new to this topic, or an experienced ML practitioner looking to brush up on an area, we're happy to have you join us! We want to create a friendly launching spot for your ML study and would be happy to evaluate, respond to, and incorporate your [feedback](https://github.com/microsoft/ML-For-Beginners/discussions). - -[![Introduction to ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML") - -> 🎥 Click the image above for a video: MIT's John Guttag introduces machine learning - ---- -## Getting started with machine learning - -Before starting with this curriculum, you need to have your computer set up and ready to run notebooks locally. - -- **Configure your machine with these videos**. Use the following links to learn [how to install Python](https://youtu.be/CXZYvNRIAKM) in your system and [setup a text editor](https://youtu.be/EU8eayHWoZg) for development. -- **Learn Python**. It's also recommended to have a basic understanding of [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), a programming language useful for data scientists that we use in this course. -- **Learn Node.js and JavaScript**. We also use JavaScript a few times in this course when building web apps, so you will need to have [node](https://nodejs.org) and [npm](https://www.npmjs.com/) installed, as well as [Visual Studio Code](https://code.visualstudio.com/) available for both Python and JavaScript development. -- **Create a GitHub account**. Since you found us here on [GitHub](https://github.com), you might already have an account, but if not, create one and then fork this curriculum to use on your own. (Feel free to give us a star, too 😊) -- **Explore Scikit-learn**. Familiarize yourself with [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), a set of ML libraries that we reference in these lessons. - ---- -## What is machine learning? - -The term 'machine learning' is one of the most popular and frequently used terms of today. There is a nontrivial possibility that you have heard this term at least once if you have some sort of familiarity with technology, no matter what domain you work in. The mechanics of machine learning, however, are a mystery to most people. For a machine learning beginner, the subject can sometimes feel overwhelming. Therefore, it is important to understand what machine learning actually is, and to learn about it step by step, through practical examples. - ---- -## The hype curve - -![ml hype curve](images/hype.png) - -> Google Trends shows the recent 'hype curve' of the term 'machine learning' - ---- -## A mysterious universe - -We live in a universe full of fascinating mysteries. Great scientists such as Stephen Hawking, Albert Einstein, and many more have devoted their lives to searching for meaningful information that uncovers the mysteries of the world around us. This is the human condition of learning: a human child learns new things and uncovers the structure of their world year by year as they grow to adulthood. - ---- -## The child's brain - -A child's brain and senses perceive the facts of their surroundings and gradually learn the hidden patterns of life which help the child to craft logical rules to identify learned patterns. The learning process of the human brain makes humans the most sophisticated living creature of this world. Learning continuously by discovering hidden patterns and then innovating on those patterns enables us to make ourselves better and better throughout our lifetime. This learning capacity and evolving capability is related to a concept called [brain plasticity](https://www.simplypsychology.org/brain-plasticity.html). Superficially, we can draw some motivational similarities between the learning process of the human brain and the concepts of machine learning. - ---- -## The human brain - -The [human brain](https://www.livescience.com/29365-human-brain.html) perceives things from the real world, processes the perceived information, makes rational decisions, and performs certain actions based on circumstances. This is what we called behaving intelligently. When we program a facsimile of the intelligent behavioral process to a machine, it is called artificial intelligence (AI). - ---- -## Some terminology - -Although the terms can be confused, machine learning (ML) is an important subset of artificial intelligence. **ML is concerned with using specialized algorithms to uncover meaningful information and find hidden patterns from perceived data to corroborate the rational decision-making process**. - ---- -## AI, ML, Deep Learning - -![AI, ML, deep learning, data science](images/ai-ml-ds.png) - -> A diagram showing the relationships between AI, ML, deep learning, and data science. Infographic by [Jen Looper](https://twitter.com/jenlooper) inspired by [this graphic](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining) - ---- -## Concepts to cover - -In this curriculum, we are going to cover only the core concepts of machine learning that a beginner must know. We cover what we call 'classical machine learning' primarily using Scikit-learn, an excellent library many students use to learn the basics. To understand broader concepts of artificial intelligence or deep learning, a strong fundamental knowledge of machine learning is indispensable, and so we would like to offer it here. - ---- -## In this course you will learn: - -- core concepts of machine learning -- the history of ML -- ML and fairness -- regression ML techniques -- classification ML techniques -- clustering ML techniques -- natural language processing ML techniques -- time series forecasting ML techniques -- reinforcement learning -- real-world applications for ML - ---- -## What we will not cover - -- deep learning -- neural networks -- AI - -To make for a better learning experience, we will avoid the complexities of neural networks, 'deep learning' - many-layered model-building using neural networks - and AI, which we will discuss in a different curriculum. We also will offer a forthcoming data science curriculum to focus on that aspect of this larger field. - ---- -## Why study machine learning? - -Machine learning, from a systems perspective, is defined as the creation of automated systems that can learn hidden patterns from data to aid in making intelligent decisions. - -This motivation is loosely inspired by how the human brain learns certain things based on the data it perceives from the outside world. - -✅ Think for a minute why a business would want to try to use machine learning strategies vs. creating a hard-coded rules-based engine. - ---- -## Applications of machine learning - -Applications of machine learning are now almost everywhere, and are as ubiquitous as the data that is flowing around our societies, generated by our smart phones, connected devices, and other systems. Considering the immense potential of state-of-the-art machine learning algorithms, researchers have been exploring their capability to solve multi-dimensional and multi-disciplinary real-life problems with great positive outcomes. - ---- -## Examples of applied ML - -**You can use machine learning in many ways**: - -- To predict the likelihood of disease from a patient's medical history or reports. -- To leverage weather data to predict weather events. -- To understand the sentiment of a text. -- To detect fake news to stop the spread of propaganda. - -Finance, economics, earth science, space exploration, biomedical engineering, cognitive science, and even fields in the humanities have adapted machine learning to solve the arduous, data-processing heavy problems of their domain. - ---- -## Conclusion - -Machine learning automates the process of pattern-discovery by finding meaningful insights from real-world or generated data. It has proven itself to be highly valuable in business, health, and financial applications, among others. - -In the near future, understanding the basics of machine learning is going to be a must for people from any domain due to its widespread adoption. - ---- -# 🚀 Challenge - -Sketch, on paper or using an online app like [Excalidraw](https://excalidraw.com/), your understanding of the differences between AI, ML, deep learning, and data science. Add some ideas of problems that each of these techniques are good at solving. - -# [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/) - ---- -# Review & Self Study - -To learn more about how you can work with ML algorithms in the cloud, follow this [Learning Path](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott). - -Take a [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) about the basics of ML. - ---- -# Assignment - -[Get up and running](assignment.md) diff --git a/1-Introduction/1-intro-to-ML/assignment.md b/1-Introduction/1-intro-to-ML/assignment.md deleted file mode 100644 index dbb448c3..00000000 --- a/1-Introduction/1-intro-to-ML/assignment.md +++ /dev/null @@ -1,9 +0,0 @@ -# Get Up and Running - -## Instructions - -In this non-graded assignment, you should brush up on Python and get your environment up and running and able to run notebooks. - -Take this [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), and then get your systems setup by going through these introductory videos: - -https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/images/ai-ml-ds.png b/1-Introduction/1-intro-to-ML/images/ai-ml-ds.png deleted file mode 100644 index 11c9bf3767955eedb662fa6a4430064babeec5cc..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 60304 zcmb@tWmsE5*Djm{PjP~~7I%ufLvi=uQrw*&h2rk+S_l*<#VJs%#T^Qi7I$yaLmzve z>;2C8_hqhR&z@Q9UNURXn#?58>ZU=lC@0KiaGkkJGHfVI!>Bvj<*7VJ7Tgy##Q zt)z-10MMBD;@%wT`JT#BK~n_)@Mi!3U=aYo?Q;`s7Xa|$1OWD-0Dw>y06^%P*RCP* z+!0}|uV|y90$_fwqXLkD$bi3yKmh3Z1NUEb;B%b>@E7^H2G9kP{-Pde|Kk}0eKVQ&X6%0H80LZic5EWqBXgmi1 z0BW_@*7wv`c`an=;>2!lx)7+QM*@O0kG~f{|H&%t;^^Y8QZ?X91O^&i~Nw*Eu>f4BXMUWDWC$o|(z 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zdbZR_J$gt>ar`P{v{4yhJGxe72A33;f^Hqqctq!bW$7W0^OH-mPD99Xy99bBLd z^$9-w4L;(-N{GGg^))1&a1p8MJl@bw?k|N^f20mB)58{w#ApigX diff --git a/1-Introduction/1-intro-to-ML/translations/README.bn.md b/1-Introduction/1-intro-to-ML/translations/README.bn.md deleted file mode 100644 index 5d1ac4dc..00000000 --- a/1-Introduction/1-intro-to-ML/translations/README.bn.md +++ /dev/null @@ -1,151 +0,0 @@ -# মেশিন লার্নিং এর সূচনা - - -[![ML, AI, deep learning - What's the difference?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - What's the difference?") - -> 🎥 মেশিন লার্নিং, এআই(আর্টিফিশিয়াল ইন্টিলিজেন্স) এবং ডিপ লার্নিং এর মধ্যে পার্থক্য এর আলোচনা জানতে উপরের ছবিটিতে ক্লিক করে ভিডিওটি দেখুন। - -## [প্রি-লেকচার-কুইজ](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/) - ---- -বিগিনারদের জন্য ক্লাসিক্যাল মেশিন লার্নিং কোর্স এ আপনাকে স্বাগতম!আপনি হয় এই বিষয়ে সম্পূর্ণ নতুন অথবা মেশিন লার্নিং এ নিজের অনুশীলনকে আরও উন্নত করতে চান, আপনি আমাদের সাথে যোগদান করতে পেরে আমরা খুশি! আমরা আপনার ML অধ্যয়নের জন্য একটি বন্ধুত্বপূর্ণ লঞ্চিং স্পট তৈরি করতে চাই এবং আপনার মূল্যায়ন, প্রতিক্রিয়া,[ফিডব্যাক](https://github.com/microsoft/ML-For-Beginners/discussions). জানাতে এবং অন্তর্ভুক্ত করতে পেরে খুশি হব । - - -[![Introduction to ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML") - - -> 🎥 ভিডিওটি দেখার জন্য উপরের ছবিতে ক্লিক করুন -MIT এর জন গাটেং মেশিন লার্নিং এর পরিচিতি করাচ্ছেন। - ---- -## মেশিন লার্নিং এর শুরু - -এই ক্যারিকুলাম শুরুর করার পূর্বে, নোটবুক রান করার জন্য নোটবুক সেটআপ থাকতে হবে। - - -- **আপনার মেশিন কে কনফিগার করুন এই ভিডিও দেখে**. শিখার জন্য এই লিংকটি ব্যবহার করুন [কিভাবে পাইথন ইন্সটল করতে হয়](https://youtu.be/CXZYvNRIAKM) এবং [সেটআপ এ ইডিটর](https://youtu.be/EU8eayHWoZg) . -- **পাইথন শিখুন**. [পাইথন](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) এর ব্যাসিক নলেজ জানা থাকা জরুরী। এই কোর্সের প্রোগ্রামিং ল্যাঙ্গুয়েজ ডেটা সাইন্সটিস্ট এর জন্য খুবই গুরুত্বপূর্ণ। -- **Node.js এবং JavaScript শিখুন**.ওয়েব অ্যাপস তৈরির জন্য এই কোর্সে আমরা জাবাস্ক্রিপট ব্যাবহার করব। তাই, আপনার [নোড](https://nodejs.org) এবং [npm](https://www.npmjs.com/) ইন্সটল থাকতে হবে। অন্যদিকে, পাইথন এবং জাভাস্ক্রিপট ডেভেলাপমেন্টের জন্য [ভিজুয়াল স্টুডিও](https://code.visualstudio.com/) কোড এ দুটুই আছে। -- **একটি গিটহাব অ্যাকাউন্ট তৈরি করুন**. যেহেতু আপনি আমাদের কে [গিটহাব](https://github.com) এ পেয়েছেন, তারমানে আপনার ইতিমধ্যেই একাউন্ট আছে। তবে যদি না থাকে, একটি একাউন্ট তৈরি করুন এবং পরে ফর্ক করে আপনার বানিয়ে নিন। (স্টার দিতে ভুলে যাবেন না,😊 ) -- **ঘুরিয়ে আসেন Scikit-learn**. নিজেকে পরিচিত করুন [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) এর সাথে, মেশিন লার্নিং লাইব্রেরি সেট যা আমরা এই কোর্সে উল্লেখ করে থাকব - ---- -## মেশিন লার্নিং কি? -'মেশিন লার্নিং' শব্দটি বর্তমান সময়ের সবচেয়ে জনপ্রিয় এবং প্রায়ই ব্যবহৃত একটি শব্দ। আপনি যে ডোমেইনে কাজ করেন না কেন প্রযুক্তির সাথে আপনার পরিচিতি থাকলে অন্তত একবার এই শব্দটি শুনেছেন এমন একটি অপ্রয়োজনীয় সম্ভাবনা রয়েছে। মেশিন লার্নিং এর মেকানিক্স, যাইহোক, বেশিরভাগ মানুষের কাছে এটি একটি রহস্য। একজন মেশিন লার্নিং নতুনদের জন্য, বিষয়টি কখনও কখনও অপ্রতিরোধ্য মনে হতে পারে। অতএব, মেশিন লার্নিং আসলে কী তা বোঝা গুরুত্বপূর্ণ এবং বাস্তব উদাহরণের মাধ্যমে ধাপে ধাপে এটি সম্পর্কে শিখতে হবে। - ---- -## হাইফ কার্ভ - -![ml hype curve](../images/hype.png) - -> Google Trends এ 'মেশিন লার্নিং' শব্দটির সাম্প্রতিক 'হাইপ কার্ভ'। - ---- -## এক রহস্যময় মহাবিশ্ব - -আমরা রহস্যে ভরপুর একটি আকর্ষনীয় মহাবিশ্বে বাস করি। স্টিফেন হকিং, আলবার্ট আইনস্টাইন এবং আরও অনেকের মতো মহান বিজ্ঞানীরা আমাদের চারপাশের বিশ্বের রহস্য উন্মোচন করে এমন অর্থপূর্ণ তথ্য অনুসন্ধানে তাদের জীবন উৎসর্গ করেছেন।এটি মানুষের শেখার একটি অবস্থা: একটি মানব শিশু নতুন জিনিস শিখে এবং বছরের পর বছর তাদের বিশ্বের গঠন উন্মোচন করে যখন তারা প্রাপ্তবয়স্ক হয়ে ওঠে। - ---- - -## শিশুদের মস্তিষ্ক -একটি শিশুর মস্তিষ্ক এবং ইন্দ্রিয়গুলি তাদের আশেপাশের ঘটনাগুলি উপলব্ধি করে এবং ধীরে ধীরে জীবনের লুকানো নিদর্শনগুলি শিখে যা শিশুকে শেখা নিদর্শনগুলি সনাক্ত করার জন্য যৌক্তিক নিয়ম তৈরি করতে সহায়তা করে। এই প্রথিবীতে মানুষের মস্তিষ্কের শেখার প্রক্রিয়া অন্যান্য প্রাণি থেকে খুবই অত্যাধুনিক। ক্রমাগত শেখা এবং লুকানো প্যাটার্নগুলি আবিষ্কার করে এবং তারপর সেই প্যাটার্নগুলিতে উদ্ভাবন করে আমাদের সারা জীবন জুড়ে নিজেদের আরও ভালো এবং উন্নত করতে সক্ষম করে। এই শেখার ক্ষমতা ও বিকশিত হওয়ার সক্ষমতা কে বলে [ব্রেইন প্লাস্টিসিটি](https://www.simplypsychology.org/brain-plasticity.html)। বাহ্যিকভাবে, আমরা মানব মস্তিষ্কের শেখার প্রক্রিয়া এবং মেশিন লার্নিং ধারণার মধ্যে কিছু অনুপ্রেরণামূলক মিল আঁকতে পারি। - ---- -## মানুষের মস্তিষ্ক - -[মানুষের মস্তিষ্ক]((https://www.livescience.com/29365-human-brain.html)) বাস্তব জগত থেকে জিনিসগুলি উপলব্ধি করে, অনুভূত তথ্য প্রক্রিয়া করে, যৌক্তিক সিদ্ধান্ত নেয় এবং পরিস্থিতির উপর ভিত্তি করে কিছু ক্রিয়া সম্পাদন করে। এটাকেই আমরা বলি বুদ্ধিমত্তার সাথে আচরণ করা। যখন আমরা একটি মেশিনে বুদ্ধিমান আচরণগত প্রক্রিয়ার একটি প্রতিকৃতি প্রোগ্রাম করি, তখন এটিকে কৃত্রিম বুদ্ধিমত্তা (AI) বলা হয়। - ---- -## কিছু পরিভাষা - -যদিও এটা বিভ্রান্তকর হতে পারে, মেশিন লার্নিং (এম. এল) আর্টিফিশিয়াল ইন্টিলিজেন্স এর একটি অংশ। **ML অর্থপূর্ণ তথ্য উন্মোচন করার জন্য বিশেষ অ্যালগরিদম ব্যবহার করে এবং যুক্তিসঙ্গত সিদ্ধান্ত গ্রহণের প্রক্রিয়াটিকে সমর্থন করার জন্য অনুভূত ডেটা থেকে লুকানো নিদর্শনগুলি খুঁজে বের করার সাথে সম্পর্কিত।** - ---- -## এ আই, এম এল, মেশিন লার্নিং - -![এ আই, এম এল, মেশিন লার্নিং, ডেটা সাইন্স](../images/ai-ml-ds.png) - -> ডায়াগ্রামটি এআই,এমএল, ডিপ লার্নিং এবং ডেটা সাইন্স এর মধ্যে সম্পর্ক বুঝাচ্ছে। ইনফোগ্রাফিক করেছেন [জেন লুপার](https://twitter.com/jenlooper) এবং [এই গ্রাফিক](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining) থেকে অনুপ্রাণিত হয়েছেন। - ---- -## কভার-ধারণা - -এই কারিকুলামে,আমরা শুধু মেশিন লার্নিং এর মুল ধারনা গুলো আলোচনা করব যা একজন নতুন শিক্ষার্থীর জানা প্রয়োজন। আমরা যাকে 'ক্লাসিক্যাল মেশিন লার্নিং' বলি তা আমরা প্রাথমিকভাবে Scikit-learn ব্যবহার করে কভার করি, একটি চমৎকার লাইব্রেরি যা অনেক শিক্ষার্থী মৌলিক বিষয়গুলি শিখতে ব্যবহার করে। কৃত্রিম বুদ্ধিমত্তা বা গভীর শিক্ষার বিস্তৃত ধারণা বোঝার জন্য, মেশিন লার্নিংয়ের একটি শক্তিশালী মৌলিক জ্ঞান অপরিহার্য, এবং তাই আমরা এটি এখানে অফার করতে চাই। - ---- -## এই কোর্স থেকে আপনি শিখবেন: - -- মেশিন লার্নিং এর মুল ধারণা -- মেশিন লার্নিং এর ইতিহাস -- মেশিন লার্নিং এবং ভয় -- রিগ্রেশন এম এল (মেশিন লার্নিং) টেকনিকস -- ক্লাসিফিকেশন এম এল (মেশিন লার্নিং) টেকনিকস -- ক্লাস্টারিং এম এল (মেশিন লার্নিং) টেকনিকস -- ন্যাচেরাল লেঙ্গুয়েজ প্রসেসিং এম এল (মেশিন লার্নিং) টেকনিকস -- টাইম সিরিজ ফরকাস্টিং এম এল (মেশিন লার্নিং) টেকনিকস -- রিএনফোর্সমেন্ট লার্নিং -- মেশিন লার্নিং এর জন্য বাস্তব জগতের অ্যাপলিকেশন। - ---- -## কি শিখানো হবে না: - -- ডিপ লার্নিং -- নিউরাল নেটওয়ার্কস -- এ আই (আর্টিফিশিয়াল ইন্টিলিজেন্স) - - -আরও ভালো শিখার অভিজ্ঞতা তৈরি করার জন্য, আমরা নিউরাল নেটওয়ার্ক এবং 'ডিপ লার্নিং'এর জটিলতাগুলি এড়াব - নিউরাল নেটওয়ার্ক ব্যবহার করে বহু-স্তর বিশিষ্ট মডেল-বিল্ডিং - এবং এআই, যা আমরা একটি ভিন্ন পাঠ্যক্রমে আলোচনা করব। আমরা এই বৃহত্তর প্লাটফর্মটির দিকের উপর ফোকাস করার জন্য একটি আসন্ন ডেটা সায়েন্স পাঠ্যক্রমও অফার করব। - ---- -## কেন মেশিন লার্নিং? - -মেশিন লার্নিং, একটি সিস্টেমের দৃষ্টিকোণ থেকে, স্বয়ংক্রিয় সিস্টেমের সৃষ্টি হিসাবে সংজ্ঞায়িত করা হয় যা বুদ্ধিমান সিদ্ধান্ত নিতে সহায়তা করার জন্য ডেটা থেকে লুকানো প্যাটার্নগুলি শিখতে পারে। - -এই অনুপ্রেরণাটি ঢিলেঢালাভাবে অনুপ্রাণিত হয় কিভাবে মানুষের মস্তিষ্ক বাইরের জগত থেকে প্রাপ্ত তথ্যের ভিত্তিতে কিছু জিনিস শিখে। - -✅ এক মিনিটের জন্য চিন্তা করুন কেন একটি ব্যবসা ’মেশিন লার্নিং’ কৌশল ব্যবহার করতে চায় যেখানে একটি হার্ড-কোডেড নিয়ম-ভিত্তিক ইঞ্জিন তৈরি করা যায় । - ---- -## মেশিন লার্নিং এর অ্যাপ্লিকেশন - -মেশিন লার্নিং এর অ্যাপ্লিকেশন এখন প্রায় সবখানে, এবং আমাদের সমাজের চারপাশে প্রচলিত ডেটার মতই সর্বব্যাপী, আমাদের স্মার্ট ফোন, সংযুক্ত ডিভাইস এবং অন্যান্য সিস্টেম দ্বারা উত্পন্ন। অত্যাধুনিক মেশিন লার্নিং অ্যালগরিদমের অপার সম্ভাবনার কথা বিবেচনা করে, গবেষকরা বহুমাত্রিক এবং বহু-বিষয়ক বাস্তব-জীবনের সমস্যার সমাধান করার জন্য তাদের সক্ষমতা অন্বেষণ করে চলেছেন যার মাধ্যমে বড় ইতিবাচক ফলাফল পাওয়া যাবে। - ---- -## ব্যবহৃত মেশিন লার্নিং এর উদাহরণ - -**আপনি মেশিন লার্নিং বিভিন্ন মাধ্যমে ব্যবহার করতে পারবেন**: - -- রোগীর চিকিৎসার রিপোর্ট থেকে রোগের সম্ভাবনা অনুমান করা। -- দিতে আবহাওয়ার ডেটা ব্যবহার করে আবহাওয়া এর পূর্বাভাস দেওয়া -- একটি পাঠ্যের অনুভূতি বোঝার জন্য। -- অপপ্রচার বন্ধ করতে ভুয়া খবর শনাক্ত করা। - -অর্থ, অর্থনীতি, আর্থ সায়েন্স, স্পেস এক্সপ্লোরেশন, বায়োমেডিকেল ইঞ্জিনিয়ারিং, জ্ঞানীয় বিজ্ঞান এবং এমনকি মানবিক ক্ষেত্রগুলি তাদের ডোমেনের কঠিন, ডেটা-প্রসেসিং ভারী সমস্যাগুলি সমাধান করার জন্য মেশিন লার্নিংকে ব্যবহার করেছে। - ---- -## উপসংহার - -মেশিন লার্নিং বাস্তব-বিশ্ব বা উৎপন্ন ডেটা থেকে অর্থপূর্ণ অন্তর্দৃষ্টি খোঁজার মাধ্যমে প্যাটার্ন-আবিষ্কারের প্রক্রিয়াটিকে স্বয়ংক্রিয় করে। এটি অন্যদের মধ্যে ব্যবসা, স্বাস্থ্য এবং আর্থিক অ্যাপ্লিকেশনগুলিতে অত্যন্ত মূল্যবান বলে প্রমাণিত হয়েছে। - -অদূর ভবিষ্যতে, মেশিন লার্নিংয়ের বুনিয়াদিগুলি বোঝা যে কোনও ডোমেনের লোকেদের জন্য এটির ব্যাপক গ্রহণের কারণে অপরিহার্য হতে চলেছে। - ---- -# 🚀 Challenge - -স্কেচ, কাগজে বা একটি অনলাইন অ্যাপ ব্যবহার করে [এক্সালিড্র](https://excalidraw.com/) AI, ML, ডিপ লার্নিং এবং ডেটা সায়েন্সের মধ্যে পার্থক্য সম্পর্কে। - -# [লেকচার-কুইজ](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/) - ---- -# পর্যালোচনা ও সেল্ফ স্টাডি - -আপনি কিভাবে ক্লাউডে এমএল অ্যালগরিদম দিয়ে কাজ করতে পারেন সে সম্পর্কে আরও জানতে, এটি অনুসরণ করুন [লার্নিং পাথ](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott)। - -এম এল বেসিক জানুন [লার্নিং পাথ](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) - ---- -# এসাইন্টমেন্ট - -[চলুন শুরু করি](assignment.md) \ No newline at end of file diff --git a/1-Introduction/1-intro-to-ML/translations/README.es.md b/1-Introduction/1-intro-to-ML/translations/README.es.md deleted file mode 100644 index 024dc814..00000000 --- a/1-Introduction/1-intro-to-ML/translations/README.es.md +++ /dev/null @@ -1,113 +0,0 @@ -# Introducción al machine learning - -[![ML, IA, deep learning - ¿Cuál es la diferencia?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, IA, deep learning - ¿Cuál es la diferencia?") - -> 🎥 Haz clic en la imagen de arriba para ver un video donde se discuten las diferencias entre el machine learning, la inteligencia artificial, y el deep learning. - -## [Cuestionario previo a la conferencia](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1?loc=es) - -### Introducción - -¡Te damos la bienvenida a este curso acerca del machine learning (ML) clásico para principiantes! Así se trate de tu primer contacto con este tema, o cuentes con amplia experiencia en el ML y busques refrescar tus conocimientos en un área específica, ¡nos alegramos de que te nos unas! Queremos crear un punto de lanzamiento amigable para tus estudios de ML y nos encantaría evaluar, responder, e incorporar tu [retroalimentación](https://github.com/microsoft/ML-For-Beginners/discussions). - -[![Introducción al ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introducción al ML") - -> Haz clic en la imagen de arriba para ver el video: John Guttag del MIT presenta el machine learning - -### Empezando con el machine learning - -Antes de comenzar con este currículum, debes tener tu computadora configurada y lista para ejecutar los notebooks localmente. - -- **Configura tu equipo con estos videos**. Aprende más acerca de como configurar tu equipo con [estos videos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6). -- **Aprende Python**. También se recomienda que tengas un entendimiento básico de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), un lenguaje de programación útil para practicantes de la ciencia de datos, y que se utiliza en este curso. -- **Aprende Node.js y JavaScript**. También usamos JavaScript unas cuantas veces en este curso cuando creamos aplicaciones web, así que necesitarás tener [node](https://nodejs.org) y [npm](https://www.npmjs.com/) instalados, así como [Visual Studio Code](https://code.visualstudio.com/) listo para el desarrollo con Python y JavaScript. -- **Crea una cuenta de GitHub**. Como nos encontraste aquí en [GitHub](https://github.com), puede que ya tengas una cuenta, pero si no, créate una y después haz un fork de este curriculum para usarlo en tu computadora personal. (Siéntete libre de darnos una estrella 😊) -- **Explora Scikit-learn**. Familiarízate con [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), un conjunto de bibliotecas de ML que referenciamos en estas lecciones. - -### ¿Qué es el machine learning? - -El término "machine learning" es uno de los términos más frecuentemente usados y populares hoy en día. Es muy probable que hayas escuchado este término al menos una vez si tienes algún tipo de familiaridad con la tecnología, no importa el sector en que trabajes. Aún así, las mecánicas del machine learning son un misterio para la mayoría de la gente. Para un principiante en machine learning, el tema puede parecer intimidante. Es por esto que es importante entender lo que realmente es el machine learning y aprender sobre el tema poco a poco, a través de ejemplos prácticos. - -![curva de interés en ml](../images/hype.png) - -> Google Trends nos muestra la "curva de interés" más reciente para el término "machine learning" - -Vivimos en un universo lleno de misterios fascinantes. Grandes científicos como Stephen Hawking, Albert Einstein, y muchos más han dedicado sus vidas a la búsqueda de información significativa que revela los misterios del mundo a nuestro alrededor. Esta es la condición humana del aprendizaje: un niño humano aprende cosas nuevas y descubre la estructura de su mundo año tras año a medida que se convierten en adultos. - -El cerebro y los sentidos de un niño perciben sus alrededores y van aprendiendo gradualmente los patrones escondidos de la vida, lo que le ayuda al niño a crear reglas lógicas para identificar los patrones aprendidos. El proceso de aprendizaje del cerebro humano nos hace las criaturas más sofisticadas del planeta. Aprender de forma continua al descubrir patrones ocultos e innovar sobre esos patrones nos permite seguir mejorando a lo largo de nuestras vidas. Esta capacidad de aprendizaje y la capacidad de evolución están relacionadas a un concepto llamado [plasticidad cerebral o neuroplasticidad](https://www.simplypsychology.org/brain-plasticity.html). Podemos trazar algunas similitudes superficiales en cuanto a la motivación entre el proceso de aprendizaje del cerebro humano y los conceptos de machine learning. - -El [cerebro humano](https://www.livescience.com/29365-human-brain.html) percibe cosas del mundo real, procesa la información percibida, toma decisiones racionales, y realiza ciertas acciones basadas en las circunstancias. Esto es a lo que se le conoce como el comportamiento inteligente. Cuando programamos un facsímil (copia) del proceso del comportamiento inteligente, se le llama inteligencia artificial (IA). - -Aunque los términos se suelen confundir, machine learning (ML) es una parte importante de la inteligencia artificial. **El objetivo del ML es utilizar algoritmos especializados para descubrir información significativa y encontrar patrones ocultos de los datos percibidos para corroborar el proceso relacional de la toma de decisiones**. - -![IA, ML, deep learning, ciencia de los datos](../images/ai-ml-ds.png) - -> El diagrama muestra la relación entre IA, ML, deep learning y la ciencia de los datos. Infografía hecha por [Jen Looper](https://twitter.com/jenlooper) inspirada en [esta gráfica](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining). - -## Lo que aprenderás en el curso - -En este currículum, vamos a cubrir solo los conceptos clave de machine learning que un principiante debería conocer. Cubrimos algo a lo que le llamamos "machine learning clásico" usando principalmente Scikit-learn, una biblioteca excelente que muchos estudiantes utilizan para aprender las bases. Para entender conceptos más amplios de la inteligencia artificial o deep learning, es indispensable tener un fuerte conocimiento de los fundamentos, y eso es lo que nos gustaría ofrecerte aquí. - -En este curso aprenderás: - -- conceptos clave del machine learning -- la historia del ML -- la justicia y el ML -- técnicas de regresión en ML -- técnicas de clasificación en ML -- técnicas de agrupamiento en ML -- técnicas de procesamiento del lenguaje natural en ML -- técnicas de previsión de series temporales en ML -- reforzamiento del aprendizaje -- ML aplicada al mundo real - -## Lo que no cubriremos - -- deep learning -- redes neuronales -- inteligencia artificial (IA) - -Para tener una mejor experiencia de aprendizaje, vamos a evitar las complejidades de las redes neuronales, "deep learning" (construcción de modelos de muchas capas utilizando las redes neuronales) e inteligencia artificial, que se discutirá en un currículum diferente. En un futuro también ofreceremos un currículum acerca de la ciencia de datos para enfocarnos en ese aspecto de ese campo. - -## ¿Por qué estudiar machine learning? - -El Machine learning, desde una perspectiva de los sistemas, se define como la creación de sistemas automáticos que pueden aprender patrones ocultos a partir de datos para ayudar en tomar decisiones inteligentes. - -Esta motivación está algo inspirada por como el cerebro humano aprende ciertas cosas basadas en los datos que percibe en el mundo real. - -✅ Piensa por un minuto en porqué querría un negocio intentar implementar estrategias de machine learning en lugar de programar un motor basado en reglas programadas de forma rígida. - -### Aplicaciones del machine learning - -Las aplicaciones del machine learning hoy en día están casi en todas partes, y son tan ubicuas como los datos que fluyen alrededor de nuestras sociedades, generados por nuestros teléfonos inteligentes, dispositivos conectados a internet, y otros sistemas. Considerando el inmenso potencial de los algoritmos punteros de machine learning, los investigadores han estado explorando su capacidad de resolver problemas multidimensionales y multidisciplinarios de la vida real con resultados muy positivos. - -**Tú puedes utilizar machine learning de muchas formas**: - -- Para predecir la probabilidad de enfermedad a partir del historial médico o reportes de un paciente. -- Para aprovechar datos del clima y predecir eventos climatológicos. -- Para entender la intención de un texto. -- Para detectar noticias falsas y evitar la propagación de propaganda. - -Finanzas, economía, ciencias de la Tierra, exploración espacial, ingeniería biomédica, ciencia cognitiva, e incluso campos en las humanidades han adaptado machine learning para solucionar algunos de los problemas más arduos y pesados en cuanto al procesamiento de datos de cada una de estas ramas. - -Machine learning automatiza el proceso del descubrimiento de patrones al encontrar perspectivas significativas de datos provenientes del mundo real o generados. Machine learning ha demostrado ser muy valioso en las aplicaciones del sector de la salud, de negocios y finanzas, entre otros. - -En el futuro próximo, entender las bases de machine learning va a ser una necesidad para la gente en cualquier sector debido a su adopción tan extendida. - ---- - -## 🚀 Desafío - -Dibuja, en papel o usando una aplicación como [Excalidraw](https://excalidraw.com/), cómo entiendes las diferencias entre inteligencia artificial, ML, deep learning, y la ciencia de datos. Agrega algunas ideas de problemas que cada una de estas técnicas son buenas en resolver. - -## [Cuestionario después de la lección](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2?loc=es) - -## Revisión y autoestudio - -Para aprender más sobre como puedes trabajar con algoritmos de ML en la nube, sigue esta [Ruta de Aprendizaje](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott). - -Toma esta [Ruta de Aprendizaje](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) sobre las bases de ML. - -## Tarea - -[Ponte en marcha](assignment.md) diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md deleted file mode 100644 index b9c7fb31..00000000 --- a/1-Introduction/1-intro-to-ML/translations/README.fr.md +++ /dev/null @@ -1,109 +0,0 @@ -# Introduction au machine learning - -[![ML, AI, deep learning - Quelle est la différence ?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - What's the difference?") - -> 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo expliquant la différence entre machine learning, AI et deep learning. - -## [Quiz préalable](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1?loc=fr) - -### Introduction - -Bienvenue à ce cours sur le machine learning classique pour débutant ! Que vous soyez complètement nouveau sur ce sujet ou que vous soyez un professionnel du ML expérimenté cherchant à peaufiner vos connaissances, nous sommes heureux de vous avoir avec nous ! Nous voulons créer un tremplin chaleureux pour vos études en ML et serions ravis d'évaluer, de répondre et d'apprendre de vos retours d'[expériences](https://github.com/microsoft/ML-For-Beginners/discussions). - -[![Introduction au ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML") - -> 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo: John Guttag du MIT introduit le machine learning -### Débuter avec le machine learning - -Avant de commencer avec ce cours, vous aurez besoin d'un ordinateur configuré et prêt à faire tourner des notebooks (jupyter) localement. - -- **Configurer votre ordinateur avec ces vidéos**. Apprendre comment configurer votre ordinateur avec cette [série de vidéos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6). -- **Apprendre Python**. Il est aussi recommandé d'avoir une connaissance basique de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), un langage de programmaton utile pour les data scientist que nous utilisons tout au long de ce cours. -- **Apprendre Node.js et Javascript**. Nous utilisons aussi Javascript par moment dans ce cours afin de construire des applications WEB, vous aurez donc besoin de [node](https://nodejs.org) et [npm](https://www.npmjs.com/) installé, ainsi que de [Visual Studio Code](https://code.visualstudio.com/) pour développer en Python et Javascript. -- **Créer un compte GitHub**. Comme vous nous avez trouvé sur [GitHub](https://github.com), vous y avez sûrement un compte, mais si non, créez en un et répliquez ce cours afin de l'utiliser à votre grés. (N'oublier pas de nous donner une étoile aussi 😊) -- **Explorer Scikit-learn**. Familiariser vous avec [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), un ensemble de librairies ML que nous mentionnons dans nos leçons. - -### Qu'est-ce que le machine learning - -Le terme `machine learning` est un des mots les plus populaire et le plus utilisé ces derniers temps. Il y a une probabilité accrue que vous l'ayez entendu au moins une fois si vous avez une appétence pour la technologie indépendamment du domaine dans lequel vous travaillez. Le fonctionnement du machine learning, cependant, reste un mystère pour la plupart des personnes. Pour un débutant en machine learning, le sujet peut nous submerger. Ainsi, il est important de comprendre ce qu'est le machine learning et de l'apprendre petit à petit au travers d'exemples pratiques. - -![ml hype curve](../images/hype.png) - -> Google Trends montre la récente 'courbe de popularité' pour le mot 'machine learning' - -Nous vivons dans un univers rempli de mystères fascinants. De grands scientifiques comme Stephen Hawking, Albert Einstein et pleins d'autres ont dévoués leur vie à la recherche d'informations utiles afin de dévoiler les mystères qui nous entourent. C'est la condition humaine pour apprendre : un enfant apprend de nouvelles choses et découvre la structure du monde année après année jusqu'à qu'ils deviennent adultes. - -Le cerveau d'un enfant et ses sens perçoivent l'environnement qui les entourent et apprennent graduellement des schémas non observés de la vie qui vont l'aider à fabriquer des règles logiques afin d'identifier les schémas appris. Le processus d'apprentissage du cerveau humain est ce que rend les hommes comme la créature la plus sophistiquée du monde vivant. Apprendre continuellement par la découverte de schémas non observés et ensuite innover sur ces schémas nous permet de nous améliorer tout au long de notre vie. Cette capacité d'apprendre et d'évoluer est liée au concept de [plasticité neuronale](https://www.simplypsychology.org/brain-plasticity.html), nous pouvons tirer quelques motivations similaires entre le processus d'apprentissage du cerveau humain et le concept de machine learning. - -Le [cerveau humain](https://www.livescience.com/29365-human-brain.html) perçoit des choses du monde réel, assimile les informations perçues, fait des décisions rationnelles et entreprend certaines actions selon le contexte. C'est ce que l'on appelle se comporter intelligemment. Lorsque nous programmons une reproduction du processus de ce comportement à une machine, c'est ce que l'on appelle intelligence artificielle (IA). - -Bien que le terme peut être confu, machine learning (ML) est un important sous-ensemble de l'intelligence artificielle. **ML se réfère à l'utilisation d'algorithmes spécialisés afin de découvrir des informations utiles et de trouver des schémas non observés depuis des données perçues pour corroborer un processus de décision rationnel**. - -![AI, ML, deep learning, data science](../images/ai-ml-ds.png) - -> Un diagramme montrant les relations entre AI, ML, deep learning et data science. Infographie par [Jen Looper](https://twitter.com/jenlooper) et inspiré par [ce graphique](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining) - -## Ce que vous allez apprendre dans ce cours - -Dans ce cours, nous allons nous concentrer sur les concepts clés du machine learning qu'un débutant se doit de connaître. Nous parlerons de ce que l'on appelle le 'machine learning classique' en utilisant principalement Scikit-learn, une excellente librairie que beaucoup d'étudiants utilisent afin d'apprendre les bases. Afin de comprendre les concepts plus larges de l'intelligence artificielle ou du deep learning, une profonde connaissance en machine learning est indispensable, et c'est ce que nous aimerions fournir ici. - -Dans ce cours, vous allez apprendre : - -- Les concepts clés du machine learning -- L'histoire du ML -- ML et équité (fairness) -- Les techniques de régression ML -- Les techniques de classification ML -- Les techniques de regroupement (clustering) ML -- Les techniques du traitement automatique des langues (NLP) ML -- Les techniques de prédictions à partir de séries chronologiques ML -- Apprentissage renforcé -- D'applications réels du ML - -## Ce que nous ne couvrirons pas - -- Deep learning -- Neural networks -- IA - -Afin d'avoir la meilleur expérience d'apprentissage, nous éviterons les complexités des réseaux neuronaux, du 'deep learning' (construire un modèle utilisant plusieurs couches de réseaux neuronaux) et IA, dont nous parlerons dans un cours différent. Nous offirons aussi un cours à venir sur la data science pour concentrer sur cet aspect de champs très large. - -## Pourquoi etudier le machine learning ? - -Le machine learning, depuis une perspective systémique, est défini comme la création de systèmes automatiques pouvant apprendre des schémas non observés depuis des données afin d'aider à prendre des décisions intelligentes. - -Ce but est faiblement inspiré de la manière dont le cerveau humain apprend certaines choses depuis les données qu'il perçoit du monde extérieur. - -✅ Penser une minute aux raisons qu'une entreprise aurait d'essayer d'utiliser des stratégies de machine learning au lieu de créer des règles codés en dur. - -### Les applications du machine learning - -Les applications du machine learning sont maintenant pratiquement partout, et sont aussi omniprésentes que les données qui circulent autour de notre société (générés par nos smartphones, appareils connectés ou autres systèmes). En prenant en considération l'immense potentiel des algorithmes dernier cri de machine learning, les chercheurs ont pu exploités leurs capacités afin de résoudre des problèmes multidimensionnels et interdisciplinaires de la vie avec d'important retours positifs - -**Vous pouvez utiliser le machine learning de plusieurs manières** : - -- Afin de prédire la possibilité d'avoir une maladie à partir des données médicales d'un patient. -- Pour tirer parti des données météorologiques afin de prédire les événements météorologiques. -- Afin de comprendre le sentiment d'un texte. -- Afin de détecter les fake news pour stopper la propagation de la propagande. - -La finance, l'économie, les sciences de la terre, l'exploration spatiale, le génie biomédical, les sciences cognitives et même les domaines des sciences humaines ont adapté le machine learning pour résoudre les problèmes ardus et lourds de traitement des données dans leur domaine respectif. - -Le machine learning automatise le processus de découverte de modèles en trouvant des informations significatives à partir de données réelles ou générées. Il s'est avéré très utile dans les applications commerciales, de santé et financières, entre autres. - -Dans un avenir proche, comprendre les bases du machine learning sera indispensable pour les personnes de tous les domaines en raison de son adoption généralisée. - ---- -## 🚀 Challenge - -Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compréhension des différences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques idées de problèmes que chacune de ces techniques est bonne à résoudre. - -## [Quiz de validation des connaissances](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2?loc=fr) - -## Révision et auto-apprentissage - -Pour en savoir plus sur la façon dont vous pouvez utiliser les algorithmes de ML dans le cloud, suivez ce [Parcours d'apprentissage](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott). - -## Devoir - -[Être opérationnel](assignment.fr.md) diff --git a/1-Introduction/1-intro-to-ML/translations/README.id.md b/1-Introduction/1-intro-to-ML/translations/README.id.md deleted file mode 100644 index 230bddfb..00000000 --- a/1-Introduction/1-intro-to-ML/translations/README.id.md +++ /dev/null @@ -1,107 +0,0 @@ -# Pengantar Machine Learning - -[![ML, AI, deep learning - Apa perbedaannya?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - Apa perbedaannya?") - -> 🎥 Klik gambar diatas untuk menonton video yang mendiskusikan perbedaan antara Machine Learning, AI, dan Deep Learning. - -## [Quiz Pra-Pelajaran](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/) - -### Pengantar - -Selamat datang di pelajaran Machine Learning klasik untuk pemula! Baik kamu yang masih benar-benar baru, atau seorang praktisi ML berpengalaman yang ingin meningkatkan kemampuan kamu, kami senang kamu ikut bersama kami! Kami ingin membuat sebuah titik mulai yang ramah untuk pembelajaran ML kamu dan akan sangat senang untuk mengevaluasi, merespon, dan memasukkan [umpan balik](https://github.com/microsoft/ML-For-Beginners/discussions) kamu. - -[![Pengantar Machine Learning](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Pengantar Machine Learning") - -> 🎥 Klik gambar diatas untuk menonton video: John Guttag dari MIT yang memberikan pengantar Machine Learning. -### Memulai Machine Learning - -Sebelum memulai kurikulum ini, kamu perlu memastikan komputer kamu sudah dipersiapkan untuk menjalankan *notebook* secara lokal. - -- **Konfigurasi komputer kamu dengan video ini**. Pelajari bagaimana menyiapkan komputer kamu dalam [video-video](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6) ini. -- **Belajar Python**. Disarankan juga untuk memiliki pemahaman dasar dari [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), sebuah bahasa pemrograman yang digunakan oleh data scientist yang juga akan kita gunakan dalam pelajaran ini. -- **Belajar Node.js dan JavaScript**. Kita juga menggunakan JavaScript beberapa kali dalam pelajaran ini ketika membangun aplikasi web, jadi kamu perlu menginstal [node](https://nodejs.org) dan [npm](https://www.npmjs.com/), serta [Visual Studio Code](https://code.visualstudio.com/) yang tersedia untuk pengembangan Python dan JavaScript. -- **Buat akun GitHub**. Karena kamu menemukan kami di [GitHub](https://github.com), kamu mungkin sudah punya akun, tapi jika belum, silakan buat akun baru kemudian *fork* kurikulum ini untuk kamu pergunakan sendiri. (Jangan ragu untuk memberikan kami bintang juga 😊) -- **Jelajahi Scikit-learn**. Buat diri kamu familiar dengan [Scikit-learn]([https://scikit-learn.org/stable/user_guide.html), seperangkat *library* ML yang kita acu dalam pelajaran-pelajaran ini. - -### Apa itu Machine Learning? - -Istilah 'Machine Learning' merupakan salah satu istilah yang paling populer dan paling sering digunakan saat ini. Ada kemungkinan kamu pernah mendengar istilah ini paling tidak sekali jika kamu familiar dengan teknologi. Tetapi untuk mekanisme Machine Learning sendiri, merupakan sebuah misteri bagi sebagian besar orang. Karena itu, penting untuk memahami sebenarnya apa itu Machine Learning, dan mempelajarinya langkah demi langkah melalui contoh praktis. - -![kurva tren ml](../images/hype.png) - -> Google Trends memperlihatkan 'kurva tren' dari istilah 'Machine Learning' belakangan ini. - -Kita hidup di sebuah alam semesta yang penuh dengan misteri yang menarik. Ilmuwan-ilmuwan besar seperti Stephen Hawking, Albert Einstein, dan banyak lagi telah mengabdikan hidup mereka untuk mencari informasi yang berarti yang mengungkap misteri dari dunia disekitar kita. Ini adalah kondisi belajar manusia: seorang anak manusia belajar hal-hal baru dan mengungkap struktur dari dunianya tahun demi tahun saat mereka tumbuh dewasa. - -Otak dan indera seorang anak memahami fakta-fakta di sekitarnya dan secara bertahap mempelajari pola-pola kehidupan yang tersembunyi yang membantu anak untuk menyusun aturan-aturan logis untuk mengidentifikasi pola-pola yang dipelajari. Proses pembelajaran otak manusia ini menjadikan manusia sebagai makhluk hidup paling canggih di dunia ini. Belajar terus menerus dengan menemukan pola-pola tersembunyi dan kemudian berinovasi pada pola-pola itu memungkinkan kita untuk terus menjadikan diri kita lebih baik sepanjang hidup. Kapasitas belajar dan kemampuan berkembang ini terkait dengan konsep yang disebut dengan *[brain plasticity](https://www.simplypsychology.org/brain-plasticity.html)*. Secara sempit, kita dapat menarik beberapa kesamaan motivasi antara proses pembelajaran otak manusia dan konsep Machine Learning. - -[Otak manusia](https://www.livescience.com/29365-human-brain.html) menerima banyak hal dari dunia nyata, memproses informasi yang diterima, membuat keputusan rasional, dan melakukan aksi-aksi tertentu berdasarkan keadaan. Inilah yang kita sebut dengan berperilaku cerdas. Ketika kita memprogram sebuah salinan dari proses perilaku cerdas ke sebuah mesin, ini dinamakan kecerdasan buatan atau Artificial Intelligence (AI). - -Meskipun istilah-stilahnya bisa membingungkan, Machine Learning (ML) adalah bagian penting dari Artificial Intelligence. **ML berkaitan dengan menggunakan algoritma-algoritma terspesialisasi untuk mengungkap informasi yang berarti dan mencari pola-pola tersembunyi dari data yang diterima untuk mendukung proses pembuatan keputusan rasional**. - -![AI, ML, deep learning, data science](../images/ai-ml-ds.png) - -> Sebuah diagram yang memperlihatkan hubungan antara AI, ML, Deep Learning, dan Data Science. Infografis oleh [Jen Looper](https://twitter.com/jenlooper) terinspirasi dari [infografis ini](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining) - -## Apa yang akan kamu pelajari - -Dalam kurikulum ini, kita hanya akan membahas konsep inti dari Machine Learning yang harus diketahui oleh seorang pemula. Kita membahas apa yang kami sebut sebagai 'Machine Learning klasik' utamanya menggunakan Scikit-learn, sebuah *library* luar biasa yang banyak digunakan para siswa untuk belajar dasarnya. Untuk memahami konsep Artificial Intelligence atau Deep Learning yang lebih luas, pengetahuan dasar yang kuat tentang Machine Learning sangat diperlukan, itulah yang ingin kami tawarkan di sini. - -Kamu akan belajar: - -- Konsep inti ML -- Sejarah dari ML -- Keadilan dan ML -- Teknik regresi ML -- Teknik klasifikasi ML -- Teknik *clustering* ML -- Teknik *natural language processing* ML -- Teknik *time series forecasting* ML -- *Reinforcement learning* -- Penerapan nyata dari ML -## Yang tidak akan kita bahas - -- *deep learning* -- *neural networks* -- AI - -Untuk membuat pengalaman belajar yang lebih baik, kita akan menghindari kerumitan dari *neural network*, *deep learning* - membangun *many-layered model* menggunakan *neural network* - dan AI, yang mana akan kita bahas dalam kurikulum yang berbeda. Kami juga akan menawarkan kurikulum *data science* yang berfokus pada aspek bidang tersebut. -## Kenapa belajar Machine Learning? - -Machine Learning, dari perspektif sistem, didefinisikan sebagai pembuatan sistem otomatis yang dapat mempelajari pola-pola tersembunyi dari data untuk membantu membuat keputusan cerdas. - -Motivasi ini secara bebas terinspirasi dari bagaimana otak manusia mempelajari hal-hal tertentu berdasarkan data yang diterimanya dari dunia luar. - -✅ Pikirkan sejenak mengapa sebuah bisnis ingin mencoba menggunakan strategi Machine Learning dibandingkan membuat sebuah mesin berbasis aturan yang tertanam (*hard-coded*). - -### Penerapan Machine Learning - -Penerapan Machine Learning saat ini hampir ada di mana-mana, seperti data yang mengalir di sekitar kita, yang dihasilkan oleh ponsel pintar, perangkat yang terhubung, dan sistem lainnya. Mempertimbangkan potensi besar dari algoritma Machine Learning terkini, para peneliti telah mengeksplorasi kemampuan Machine Learning untuk memecahkan masalah kehidupan nyata multi-dimensi dan multi-disiplin dengan hasil positif yang luar biasa. - -**Kamu bisa menggunakan Machine Learning dalam banyak hal**: - -- Untuk memprediksi kemungkinan penyakit berdasarkan riwayat atau laporan medis pasien. -- Untuk memanfaatkan data cuaca untuk memprediksi peristiwa cuaca. -- Untuk memahami sentimen sebuah teks. -- Untuk mendeteksi berita palsu untuk menghentikan penyebaran propaganda. - -Keuangan, ekonomi, geosains, eksplorasi ruang angkasa, teknik biomedis, ilmu kognitif, dan bahkan bidang humaniora telah mengadaptasi Machine Learning untuk memecahkan masalah sulit pemrosesan data di bidang mereka. - -Machine Learning mengotomatiskan proses penemuan pola dengan menemukan wawasan yang berarti dari dunia nyata atau dari data yang dihasilkan. Machine Learning terbukti sangat berharga dalam penerapannya di berbagai bidang, diantaranya adalah bidang bisnis, kesehatan, dan keuangan. - -Dalam waktu dekat, memahami dasar-dasar Machine Learning akan menjadi suatu keharusan bagi orang-orang dari bidang apa pun karena adopsinya yang luas. - ---- -## 🚀 Tantangan - -Buat sketsa di atas kertas atau menggunakan aplikasi seperti [Excalidraw](https://excalidraw.com/), mengenai pemahaman kamu tentang perbedaan antara AI, ML, Deep Learning, dan Data Science. Tambahkan beberapa ide masalah yang cocok diselesaikan masing-masing teknik. - -## [Quiz Pasca-Pelajaran](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/) - -## Ulasan & Belajar Mandiri - -Untuk mempelajari lebih lanjut tentang bagaimana kamu dapat menggunakan algoritma ML di cloud, ikuti [Jalur Belajar](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott) ini. - -## Tugas - -[Persiapan](assignment.id.md) diff --git a/1-Introduction/1-intro-to-ML/translations/README.it.md b/1-Introduction/1-intro-to-ML/translations/README.it.md deleted file mode 100644 index 37b23a0a..00000000 --- a/1-Introduction/1-intro-to-ML/translations/README.it.md +++ /dev/null @@ -1,108 +0,0 @@ -# Introduzione a machine learning - -[![ML, AI, deep learning: qual è la differenza?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning: qual è la differenza?") - -> 🎥 Fare clic sull'immagine sopra per un video che illustra la differenza tra machine learning, intelligenza artificiale (AI) e deep learning. - -## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/?loc=it) - -### Introduzione - -Benvenuti in questo corso su machine learning classico per principianti! Che si sia completamente nuovo su questo argomento, o un professionista esperto di ML che cerca di rispolverare un'area, è un piacere avervi con noi! Si vuole creare un punto di partenza amichevole per lo studio di ML e saremo lieti di valutare, rispondere e incorporare il vostro [feedback](https://github.com/microsoft/ML-For-Beginners/discussions). - -[![Introduzione a ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 " Introduzione a ML") - -> 🎥 Fare clic sull'immagine sopra per un video: John Guttag del MIT introduce machine learning - -### Iniziare con machine learning - -Prima di iniziare con questo programma di studi, è necessario che il computer sia configurato e pronto per eseguire i notebook in locale. - -- **Si configuri la propria macchina con l'aiuto di questi video**. Si scopra di più su come configurare la propria macchina in questa [serie di video](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6). -- **Imparare Python**. Si consiglia inoltre di avere una conoscenza di base di [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), un linguaggio di programmazione utile per i data scientist che si utilizzerà in questo corso. -- **Imparare Node.js e JavaScript**. Talvolta in questo corso si usa anche JavaScript durante la creazione di app web, quindi sarà necessario disporre di [node](https://nodejs.org) e [npm](https://www.npmjs.com/) installati, oltre a [Visual Studio Code](https://code.visualstudio.com/) disponibile sia per lo sviluppo Python che JavaScript. -- **Creare un account GitHub**. E' probabile che si [](https://github.com)disponga già di un account GitHub, ma in caso contrario occorre crearne uno e poi eseguire il fork di questo programma di studi per utilizzarlo autonomamente. (Sentitevi liberi di darci anche una stella 😊) -- **Esplorare Scikit-learn**. Familiarizzare con Scikit-learn,[]([https://scikit-learn.org/stable/user_guide.html) un insieme di librerie ML a cui si farà riferimento in queste lezioni. - -### Che cos'è machine learning? - -Il termine "machine learning" è uno dei termini più popolari e usati di oggi. C'è una buona possibilità che si abbia sentito questo termine almeno una volta se si ha una sorta di familiarità con la tecnologia, indipendentemente dal campo in cui si lavora. I meccanismi di machine learning, tuttavia, sono un mistero per la maggior parte delle persone. Per un principiante di machine learning l'argomento a volte può sembrare soffocante. Pertanto, è importante capire cos'è effettivamente machine learning e impararlo passo dopo passo, attraverso esempi pratici. - -![ml curva di hype](../images/hype.png) - -> Google Trends mostra la recente "curva di hype" del termine "machine learning" - -Si vive in un universo pieno di misteri affascinanti. Grandi scienziati come Stephen Hawking, Albert Einstein e molti altri hanno dedicato la loro vita alla ricerca di informazioni significative che svelino i misteri del mondo circostante. Questa è la condizione umana dell'apprendimento: un bambino impara cose nuove e scopre la struttura del suo mondo anno dopo anno mentre cresce fino all'età adulta. - -Il cervello e i sensi di un bambino percepiscono i fatti dell'ambiente circostante e apprendono gradualmente i modelli di vita nascosti che aiutano il bambino a creare regole logiche per identificare i modelli appresi. Il processo di apprendimento del cervello umano rende l'essere umano la creatura vivente più sofisticata di questo mondo. Imparare continuamente scoprendo schemi nascosti e poi innovare su questi schemi ci consente di migliorarsi sempre di più per tutta la vita. Questa capacità di apprendimento e capacità di evoluzione è correlata a un concetto chiamato [plasticità cerebrale](https://www.simplypsychology.org/brain-plasticity.html). Superficialmente, si possono tracciare alcune somiglianze motivazionali tra il processo di apprendimento del cervello umano e i concetti di machine learning. - -Il [cervello umano](https://www.livescience.com/29365-human-brain.html) percepisce le cose dal mondo reale, elabora le informazioni percepite, prende decisioni razionali ed esegue determinate azioni in base alle circostanze. Questo è ciò che viene chiamato comportarsi in modo intelligente. Quando si programma un facsimile del processo comportamentale intelligente su una macchina, si parla di intelligenza artificiale (AI). - -Sebbene i termini possano essere confusi, machine learning (ML) è un importante sottoinsieme dell'intelligenza artificiale. **Machine learning si occupa di utilizzare algoritmi specializzati per scoprire informazioni significative e trovare modelli nascosti dai dati percepiti per corroborare il processo decisionale razionale**. - -![AI, machine learning, deep learning, data science](../images/ai-ml-ds.png) - -> Un diagramma che mostra le relazioni tra intelligenza artificiale (AI), machine learning, deep learning e data science. Infografica di [Jen Looper](https://twitter.com/jenlooper) ispirata a [questa grafica](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining) - -## Ecco cosa si imparerà in questo corso - -In questo programma di studi, saranno tratteti solo i concetti fondamentali di machine learning che un principiante deve conoscere. Si tratterà di ciò che viene chiamato "machine learning classico" principalmente utilizzando Scikit-learn, una eccellente libreria che molti studenti usano per apprendere le basi. Per comprendere concetti più ampi di intelligenza artificiale o deep learning, è indispensabile una forte conoscenza fondamentale di machine learning, e quindi la si vorrebbe offrire qui. - -In questo corso si imparerà: - -- concetti fondamentali di machine learning -- la storia di ML -- ML e correttezza -- tecniche di regressione ML -- tecniche di classificazione ML -- tecniche di clustering ML -- tecniche di elaborazione del linguaggio naturale ML -- tecniche ML di previsione delle serie temporali -- reinforcement learning -- applicazioni del mondo reale per ML -## Cosa non verrà trattato - -- deep learning -- reti neurali -- AI (intelligenza artificiale) - -Per rendere l'esperienza di apprendimento migliore, si eviteranno le complessità delle reti neurali, del "deep learning" (costruzione di modelli a più livelli utilizzando le reti neurali) e dell'AI, di cui si tratterà in un altro programma di studi. Si offrirà anche un prossimo programma di studi di data science per concentrarsi su quell'aspetto di questo campo più ampio. -## Perché studiare machine learning? - -Machine learning, dal punto di vista dei sistemi, è definito come la creazione di sistemi automatizzati in grado di apprendere modelli nascosti dai dati per aiutare a prendere decisioni intelligenti. - -Questa motivazione è vagamente ispirata dal modo in cui il cervello umano apprende determinate cose in base ai dati che percepisce dal mondo esterno. - -✅ Si pensi per un minuto al motivo per cui un'azienda dovrebbe provare a utilizzare strategie di machine learning rispetto alla creazione di un motore cablato a codice basato su regole codificate. - -### Applicazioni di machine learning - -Le applicazioni di machine learning sono ormai quasi ovunque e sono onnipresenti come i dati che circolano nelle società, generati dagli smartphone, dispositivi connessi e altri sistemi. Considerando l'immenso potenziale degli algoritmi di machine learning all'avanguardia, i ricercatori hanno esplorato la loro capacità di risolvere problemi multidimensionali e multidisciplinari della vita reale con grandi risultati positivi. - -**Si può utilizzare machine learning in molti modi**: - -- Per prevedere la probabilità di malattia dall'anamnesi o dai rapporti di un paziente. -- Per sfruttare i dati meteorologici per prevedere gli eventi meteorologici. -- Per comprendere il sentimento di un testo. -- Per rilevare notizie false per fermare la diffusione della propaganda. - -La finanza, l'economia, le scienze della terra, l'esplorazione spaziale, l'ingegneria biomedica, le scienze cognitive e persino i campi delle scienze umanistiche hanno adattato machine learning per risolvere gli ardui problemi di elaborazione dati del proprio campo. - -Machine learning automatizza il processo di individuazione dei modelli trovando approfondimenti significativi dal mondo reale o dai dati generati. Si è dimostrato di grande valore in applicazioni aziendali, sanitarie e finanziarie, tra le altre. - -Nel prossimo futuro, comprendere le basi di machine learning sarà un must per le persone in qualsiasi campo a causa della sua adozione diffusa. - ---- -## 🚀 Sfida - -Disegnare, su carta o utilizzando un'app online come [Excalidraw](https://excalidraw.com/), la propria comprensione delle differenze tra AI, ML, deep learning e data science. Aggiungere alcune idee sui problemi che ciascuna di queste tecniche è in grado di risolvere. - -## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/?loc=it) - -## Revisione e Auto Apprendimento - -Per saperne di più su come si può lavorare con gli algoritmi ML nel cloud, si segua questo [percorso di apprendimento](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott). - -## Compito - -[Tempi di apprendimento brevi](assignment.it.md) diff --git a/1-Introduction/1-intro-to-ML/translations/README.ja.md b/1-Introduction/1-intro-to-ML/translations/README.ja.md deleted file mode 100644 index db0ed50d..00000000 --- a/1-Introduction/1-intro-to-ML/translations/README.ja.md +++ /dev/null @@ -1,105 +0,0 @@ -# 機械学習への導入 - -[![ML, AI, deep learning - 違いは何か？](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - 違いは何か？") - -> 🎥 上の画像をクリックすると、機械学習、AI、深層学習の違いについて説明した動画が表示されます。 - -## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1?loc=ja) - -### イントロダクション - -初心者のための古典的な機械学習のコースへようこそ! このテーマに全く触れたことのない方も、この分野をブラッシュアップしたい経験豊富な方も、ぜひご参加ください。私たちは、あなたのMLの学習についての親しみやすいスタート地点を作りたいと考えています。あなたの[フィードバック](https://github.com/microsoft/ML-For-Beginners/discussions)を評価し、対応し、取り入れることができれば幸いです。 -[![機械学習への導入](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "機械学習への導入") - -> 🎥 上の画像をクリックすると、MITのJohn Guttagが機械学習を紹介する動画が表示されます。 -### 機械学習を始めるにあたって - -このカリキュラムを始める前に、コンピュータを設定し、ノートブックをローカルで実行できるようにする必要があります。 - -- **こちらのビデオでマシンの設定を行ってください。** マシンの設定方法については、[これらのビデオ](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)をご覧ください。 -- **Pythonを学習する。** 本講座で使用する、データサイエンティストに有用なプログラミング言語である[Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)の基本的な理解があることが望ましいです。 -- **Node.jsとJavaScriptを学習する。** このコースではウェブアプリを構築する際にJavaScriptも何度か使用しますので、[node](https://nodejs.org)と[npm](https://www.npmjs.com/)がインストールされていること、PythonとJavaScriptの両方の開発に必要な[Visual Studio Code](https://code.visualstudio.com/)が利用可能であることが必要です。 -- **GitHubのアカウントを作成する。** [GitHub](https://github.com)で私たちを見つけたのですから、すでにアカウントをお持ちかもしれませんが、もしお持ちでなければ、アカウントを作成して、このカリキュラムをフォークしてご自分でお使いください。(スターをつけることもお忘れなく😊) -- **Scikit-learnを探索する。** このレッスンで参照するMLライブラリのセットである[Scikit-learn]([https://scikit-learn.org/stable/user_guide.html)に慣れ親しんでください。 - -### 機械学習とは何か？ - -"機械学習(Machine Learning)"という言葉は、現在最も人気があり、頻繁に使用されている言葉の一つです。どんな分野の技術者であっても、多少なりとも技術に精通していれば、一度はこの言葉を耳にしたことがある可能性は少なくありません。しかし、機械学習の仕組みは、ほとんどの人にとって謎に包まれており、機械学習の初心者にとって、このテーマは時に圧倒されるように感じられます。そのため、機械学習とは何かを実際に理解し、実践的な例を通して段階的に学んでいくことが重要です。 - -![機械学習の人気を示すグラフ](../images/hype.png) - -> Google Trendsによる、「機械学習」という言葉の最近の盛り上がりを示すグラフ。 - -私たちは、魅力的な謎に満ちた宇宙に住んでいます。ホーキング博士やアインシュタイン博士をはじめとする偉大な科学者たちは、私たちを取り巻く世界の謎を解き明かす意味のある情報を探すことに人生を捧げてきました。人間の子供は、大人になるまでの間に、年々新しいことを学び、自分の世界の構造を明らかにしていきます。 - -子供の脳と感覚は、周囲の事実を認識し、徐々に人生の隠れたパターンを学び、学習したパターンを識別するための論理的なルールを作るのに役立ちます。こういった学習プロセスは、人間をこの世で最も洗練された生物にしています。隠れたパターンを発見することで継続的に学習し、そのパターンに基づいて革新を行うことで、私たちは生涯を通じて自分自身をより良くしていくことができます。この学習能力と進化能力は、[「脳の可塑性」](https://www.simplypsychology.org/brain-plasticity.html)と呼ばれる概念に関連しています。表面的には、人間の脳の学習プロセスと機械学習のコンセプトには、モチベーションの面でいくつかの共通点があります。 - -[人間の脳](https://www.livescience.com/29365-human-brain.html)は、現実世界の物事を知覚し、知覚した情報を処理し、合理的な判断を下し、状況に応じてある行動をします。これは知的行動と呼ばれます。この知的行動のプロセスを機械にプログラムすることを人工知能（AI）といいます。 - -この言葉は混同されることがありますが、機械学習（ML）は人工知能の重要なサブセットです。**MLは、特殊なアルゴリズムを使用して、意味のある情報を発見し、知覚されたデータから隠れたパターンを見つけて、合理的な意思決定プロセスを裏付けることに関係しています。** - -![AI, ML, ディープラーニング、データサイエンス](../images/ai-ml-ds.png) - - ->[このグラフ](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)に触発された[Jen Looper](https://twitter.com/jenlooper)氏によるインフォグラフィック - -## このコースで学ぶこと - -このカリキュラムでは、初心者が知っておかなければならない機械学習のコアな概念のみを取り上げます。私たちが「古典的な機械学習」と呼ぶものを、多くの学生が基礎を学ぶために使用する優れたライブラリであるScikit-learnを主に使ってカバーします。人工知能や深層学習などのより広い概念を理解するためには、機械学習の強力な基礎知識が不可欠ですので、ここで提供します。 - -- 機械学習の核となるコンセプト -- MLの歴史 -- MLと公平性 -- MLによる回帰の手法 -- MLによる分類技術 -- MLによるクラスタリング -- MLによる自然言語処理の技術 -- MLによる時系列予測の技術 -- 強化学習 -- MLの現実世界への応用 -## このコースで扱わないこと - -- ディープラーニング -- ニューラルネットワーク -- AI - -ニューラルネットワークやディープラーニング（ニューラルネットワークを用いた多層的なモデル構築）、AIなどの複雑な分野は、より良い学習環境を提供するために避けていますが、これらは別のカリキュラムで取り上げます。また、それらの大きな分野の中でも特にデータサイエンスに焦点を当てたカリキュラムを提供する予定です。 -## なぜ機械学習を学ぶのか - -機械学習とは、システムの観点から、データから隠れたパターンを学習し、知的な意思決定を支援する自動化されたシステムを構築することと定義されます。 - -この動機は、人間の脳が外界から認識したデータに基づいて特定の事柄を学習する仕組みに、ゆるやかにインスパイアされています。 - -✅ なぜビジネスでは、ハードコードされたルールベースのエンジンを作るのではなく、機械学習戦略を使ってみようと思うのか、ちょっと考えてみてください。 - - -### 機械学習の応用 - -機械学習のアプリケーションは、今やほとんどどこにでもあり、スマートフォンやコネクテッドデバイス、その他のシステムから生成され、私たちの社会に流れているデータと同様にありふれたものとなっています。最先端の機械学習アルゴリズムの計り知れない可能性を考慮して、研究者たちは、多次元的・多分野的な現実の問題を解決するためにその能力を探求し、非常に良い結果を得ています。 - -**機械学習は様々な形で利用できます**: - -- 患者の病歴や報告書から病気の可能性を予測する。 -- 気象データを活用して気象現象を予測する。 -- 文章の感情を理解する。 -- プロパガンダの拡散を防ぐためにフェイクニュースを検出する。 - -金融、経済、地球科学、宇宙開発、生物医学工学、認知科学、さらには文科系の分野でも、それぞれの分野のデータ処理に伴う困難な問題を解決するために、機械学習が採用されています。 - -機械学習は、実世界のデータや生成されたデータから意味のある洞察を見出し、パターンを発見するプロセスを自動化します。機械学習は、ビジネス、健康、金融などの分野で非常に有用であることが証明されています。 - -近い将来、機械学習の基礎を理解することは、機械学習の普及に伴い、あらゆる分野の人々にとって必須のものとなるでしょう。 - ---- -## 🚀 Challenge -AI、ML、深層学習、データサイエンスの違いについて理解していることを、紙や[Excalidraw](https://excalidraw.com/)などのオンラインアプリを使ってスケッチしてください。また、それぞれの技術が得意とする問題のアイデアを加えてみてください。 - -## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2?loc=ja) - -## 振り返りと自習 - -クラウド上でMLアルゴリズムをどのように扱うことができるかについては、この[ラーニングパス](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott)に従ってください。 - -## 課題 - -[稼働させる](assignment.ja.md) diff --git a/1-Introduction/1-intro-to-ML/translations/README.ko.md b/1-Introduction/1-intro-to-ML/translations/README.ko.md deleted file mode 100644 index eb9757d3..00000000 --- a/1-Introduction/1-intro-to-ML/translations/README.ko.md +++ /dev/null @@ -1,113 +0,0 @@ -# 머신러닝 소개 - -[![ML, AI, deep learning - What's the difference?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - What's the difference?") - -> 🎥 머신러닝, AI 그리고 딥러닝의 차이를 설명하는 영상을 보려면 위 이미지를 클릭합니다. - -## [강의 전 퀴즈](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/) - -### 소개 - -입문자를 위한 classical 머신러닝 코스에 오신 것을 환영합니다! 이 토픽에 완벽하게 새로 접해보거나, 한 분야에 완벽해지고 싶어하는 ML 실무자도 저희와 함께하게 되면 좋습니다! ML 연구를 위한 친숙한 시작점을 만들고 싶고, 당신의 [feedback](https://github.com/microsoft/ML-For-Beginners/discussions)을 평가, 응답하고 반영하겠습니다. - -[![Introduction to ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML") - -> 🎥 동영상을 보려면 위 이미지 클릭: MIT의 John Guttag가 머신러닝을 소개합니다. -### 머신러닝 시작하기 - -이 커리큘럼을 시작하기 전, 컴퓨터를 세팅하고 노트북을 로컬에서 실행할 수 있게 준비해야 합니다. - -- **이 영상으로 컴퓨터 세팅하기**. [영상 플레이리스트](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)에서 컴퓨터를 세팅하는 방법에 대하여 자세히 알아봅니다. -- **Python 배우기**. 이 코스에서 사용할 데이터 사이언티스트에게 유용한 프로그래밍 언어인 [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)에 대한 기본적인 이해를 해야 좋습니다. -- **Node.js 와 JavaScript 배우기**. 이 코스에서 웹앱을 빌드할 때 JavaScript를 사용하므로, [node](https://nodejs.org) 와 [npm](https://www.npmjs.com/)을 설치해야 합니다. Python 과 JavaScript의 개발환경 모두 쓸 수 있는 [Visual Studio Code](https://code.visualstudio.com/)도 있습니다. -- **GitHub 계정 만들기**. [GitHub](https://github.com) 계정이 혹시 없다면, 계정을 만든 뒤에 이 커리큘럼을 포크해서 개인에 맞게 쓸 수 있습니다. (star 하셔도 됩니다 😊) -- **Scikit-learn 찾아보기**. 이 강의에서 참조하고 있는 ML 라이브러리 셋인 [Scikit-learn](https://scikit-learn.org/stable/user_guide.html)을 숙지합니다. - -### 머신러닝은 무엇인가요? - -'머신러닝'은 최근 가장 인기있고 자주 언급되는 용어입니다. 어떤 분야든 기술에 어느 정도 익숙해지면 이러한 용어를 한 번즈음 들어본 적이 있었을 것입니다. 그러나, 머신러닝의 구조는 대부분의 사람들에겐 미스테리입니다. 머신러닝 입문자에겐 주제가 때때로 숨막힐 수 있습니다. 때문에 머신러닝이 실제로 어떤지 이해하고 실제 적용된 예시로 단계별 학습을 진행하는 것이 중요합니다. - -![ml hype curve](../images/hype.png) - -> Google Trends의 '머신러닝' 용어의 최근 'hype curve' 입니다. - -우리는 매우 신비한 우주에 살고 있습니다. Stephen Hawking, Albert Einstein과 같은 위대한 과학자들은 주변 세계의 신비를 밝혀낼 의미있는 정보를 찾는 데 일생을 바쳤습니다. 이건 사람의 학습 조건이죠. 아이는 성인이 되면서 해마다 새로운 것을 배우고 세계의 구조들을 발견합니다. - -아이의 뇌와 감각은 주변 환경의 사실들을 인지하고 학습된 패턴을 식별하기 위한 논리적인 규칙을 만드는 패턴을 점차적으로 배웁니다. 인간의 두뇌의 학습 과정은 인간을 세상에서 가장 정교한 생명체로 만듭니다. 숨겨진 패턴을 발견하고 그 패턴을 혁신함으로써 지속적으로 학습하는 것은 우리가 일생 동안 점점 더 나은 자신을 만들 수 있게 해줍니다. 이러한 학습 능력과 발전하는 능력은 [brain plasticity 뇌의 가소성](https://www.simplypsychology.org/brain-plasticity.html)이라고 불리는 개념과 관련이 있습니다. 피상적으로, 우리는 인간의 두뇌의 학습 과정과 기계 학습의 개념 사이에 동기부여의 유사성을 끌어낼 수 있습니다. - -[인간의 뇌](https://www.livescience.com/29365-human-brain.html)는 현실 세계의 것들을 인식하고, 인식된 정보를 처리하고, 합리적인 결정을 내리고, 상황에 따라 특정한 행동을 합니다. 이것이 우리가 지적 행동이라고 부르는 것입니다. 우리가 지능적인 행동 과정의 팩시밀리를 기계에 프로그래밍 할 때, 그것은 인공지능(AI)이라고 불립니다. - -용어가 헷갈릴 수 있지만, 머신러닝(ML)은 중요한 인공 지능의 한 부분입니다. **ML은 특수한 알고리즘을 써서 의미있는 정보를 찾고 인식한 데이터에서 숨겨진 패턴을 찾아 합리적으로 판단할 프로세스를 확실하게 수행하는 것에 집중하고 있다고 할 수 있습니다**. - -![AI, ML, deep learning, data science](../images/ai-ml-ds.png) - -> AI, ML, 딥러닝, 그리고 데이터 사이언티스 간의 관계를 보여주는 다이어그램. [이곳](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)에서 영감을 받은 [Jen Looper](https://twitter.com/jenlooper)의 인포그래픽 - -## 이 코스에서 배울 컨셉들 - -이 커리큘럼에서는 입문자가 반드시 알아야 할 머신러닝의 핵심적인 개념만 다룰 것입니다. 많은 학생들이 기초를 배우기 위해 사용하는 훌륭한 라이브러리인, Scikit-learn으로 'classical machine learning'이라고 부르는 것을 다룹니다. 인공 지능 또는 딥러닝의 대략적인 개념을 이해하려면 머신러닝에 대한 강력한 기초 지식이 꼭 필요하므로, 해당 내용을 본 강의에서 제공하고자 합니다. - -## 이 코스에서 다루는 것: - -- 머신러닝의 핵심 컨셉 -- ML 의 역사 -- ML 과 공정성 -- regression ML 기술 -- classification ML 기술 -- clustering ML 기술 -- natural language processing ML 기술 -- time series forecasting ML 기술 -- 강화 학습 -- real-world 애플리케이션 for ML - -## 다루지 않는 것: - -- 딥러닝 -- 신경망 -- AI - -우리는 더 나은 학습 경험을 만들기 위해 본 코스에서는 신경망, 신경망을 이용한 다층 모델 구축인 '딥러닝', 그리고 AI는 논의하지 않을 것입니다. 또한, 더 큰 필드에 초점을 맞추기 위하여 향후 데이터 사이언스 커리큘럼을 제공할 예정입니다. - -## 왜 머신러닝을 배우나요? - -시스템 관점에서 머신러닝은 데이터의 숨겨진 패턴을 학습하여 현명한 의사결정을 지원하는 자동화된 시스템을 만드는 것으로 정의됩니다. - -이것은 인간의 두뇌가 외부로부터 인지하는 데이터를 바탕으로 어떻게 특정한 것들을 배우는지에 의해 어느 정도 영감을 받았습니다. - -✅ 하드 코딩된 규칙 기반 엔진을 만드는 것보다 기계 학습 전략을 사용하는 이유를 잠시 생각해 봅시다. - -### 머신러닝의 애플리케이션 - -머신러닝의 응용은 이제 거의 모든 곳에 있으며, 우리의 스마트폰, 연결된 기기, 그리고 다른 시스템들에 의해 생성된 우리 사회의 방대한 데이터만큼 어디에나 존재합니다. 최첨단 머신러닝 알고리즘의 엄청난 잠재력을 고려하여 연구원들은 다차원적이고 다분야적인 실제 문제를 큰 긍정적인 결과로 해결할 수 있는 능력을 탐구하고 있습니다. - -**다양한 방식으로 머신러닝을 사용할 수 있습니다**: - -- 환자의 병력이나 보고서를 기반으로 질병 가능성을 예측합니다. -- 날씨 데이터로 계절 이벤트를 예측합니다. -- 문장의 감정을 이해합니다. -- 가짜 뉴스를 감지하고 선동을 막습니다. - -금융, 경제학, 지구 과학, 우주 탐험, 생물 공학, 인지 과학, 그리고 인문학까지 머신러닝을 적용하여 어렵고, 데이터 처리가 버거운 이슈를 해결했습니다. - -**결론**: - -머신러닝은 실제 또는 생성된 데이터에서 의미 있는 패턴을 찾는 프로세스를 자동화합니다. 무엇보다도 비즈니스, 건강 및 재무 애플리케이션에서 높은 가치를 지닌다는 것이 입증되었습니다. - -가까운 미래에, 머신러닝의 광범위한 채택으로 모든 분야의 사람들이 머신러닝의 기본을 이해하는 것이 필수적이 될 것입니다. - ---- -## 🚀 도전 - -종이에 그리거나, [Excalidraw](https://excalidraw.com/)처럼 온라인 앱을 이용하여 AI, ML, 딥러닝, 그리고 데이터 사이언스의 차이를 이해합시다. 각 기술들이 잘 해결할 수 있는 문제에 대해 아이디어를 합쳐보세요. - -## [강의 후 퀴즈](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/) - -## 리뷰 & 자기주도 학습 - -클라우드에서 ML 알고리즘을 어떻게 사용하는 지 자세히 알아보려면, [학습 경로](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott)를 따릅니다. - -ML의 기초에 대한 [학습 경로](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott)를 봅니다. - -## 과제 - -[Get up and running](../assignment.md) diff --git a/1-Introduction/1-intro-to-ML/translations/README.pt-br.md b/1-Introduction/1-intro-to-ML/translations/README.pt-br.md deleted file mode 100644 index 9b5f94c0..00000000 --- a/1-Introduction/1-intro-to-ML/translations/README.pt-br.md +++ /dev/null @@ -1,113 +0,0 @@ -# Introdução ao machine learning - -[![ML, AI, deep learning - Qual é a diferença?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - Qual é a diferença?") - -> 🎥 Clique na imagem acima para assistir um vídeo que ilustra a diferença entre machine learning, AI, e deep learning. - -## [Questionário inicial](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1?loc=ptbr) - -### Introdução - -Nossas boas vindas a este curso de machine learning clássico para iniciantes! Quer você seja completamente novo neste tópico, ou um praticante de ML experiente que esteja procurando se atualizar em uma área, estamos felizes por você se juntar a nós! Queremos criar um ponto de lançamento amigável para seu estudo de ML e ficaríamos felizes em avaliar, responder e incorporar o seu [feedback](https://github.com/microsoft/ML-For-Beginners/discussions). - -[![Introdução ao ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introdução ao ML") - -> 🎥 Clique na imagem acima para assistir: John Guttag, do MIT, apresenta o machine learning. - -### Primeiros passos com machine learning - -Antes de iniciar este curso, você precisa ter seu computador configurado e pronto para executar notebooks localmente. - -- **Configure sua máquina com estes vídeos**. Use os links a seguir para aprender [como instalar o Python](https://youtu.be/CXZYvNRIAKM) em seu sistema e [configurar um editor de texto](https://youtu.be/EU8eayHWoZg) para desenvolvimento. -- **Aprenda Python**. Também é recomendável ter um conhecimento básico de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), uma linguagem de programação útil para cientistas de dados (data scientists) que usamos neste curso. -- **Aprenda Node.js e JavaScript**. Também usamos JavaScript algumas vezes neste curso para criar aplicativos web, então você precisará ter [node](https://nodejs.org) e [npm](https://www.npmjs.com/) instalado, assim como o [Visual Studio Code](https://code.visualstudio.com/) disponível para desenvolvimento em Python e JavaScript. -- **Crie uma conta no GitHub**. Como você nos encontrou aqui no [GitHub](https://github.com),talvez você já tenha uma conta, mas se não, crie uma e faça um fork deste curso para usar por conta própria. (Sinta-se à vontade para nos dar uma estrela também 😊). -- **Explore o Scikit-learn**. Familiarize-se com o [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), um conjunto de bibliotecas de ML referenciadas nestas lições. - -### O que é machine learning? - -O termo 'machine learning' é um dos termos mais populares e usados ​​atualmente. Há uma boa chance de você já ter ouvido esse termo pelo menos uma vez se estiver familiarizado com tecnologia, independentemente do campo em que trabalha. A mecânica do aprendizado de máquina (machine learning), entretanto, é um mistério para a maioria das pessoas. Para um iniciante em machine learning, o assunto às vezes pode parecer opressor. Portanto, é importante entender o que realmente é o machine learning e aprender sobre isso passo a passo, por meio de exemplos práticos. - -![curva de hype de ml](../images/hype.png) - -> Google Trends mostra a recente 'curva de hype' do termo 'machine learning'. - -Vivemos em um universo cheio de mistérios fascinantes. Grandes cientistas como Stephen Hawking, Albert Einstein e muitos outros dedicaram suas vidas à busca de informações significativas que desvendam os mistérios do mundo ao nosso redor. Esta é a condição humana de aprendizagem: uma criança humana aprende coisas novas e descobre a estrutura de seu mundo ano a ano à medida que chega à idade adulta. - -O cérebro e os sentidos de uma criança percebem os fatos ao seu redor e gradualmente aprendem os padrões ocultos de vida que ajudam a criança a criar regras lógicas para identificar os padrões aprendidos. O processo de aprendizagem do cérebro humano torna os humanos a criatura viva mais sofisticada deste mundo. Aprender continuamente, descobrindo padrões ocultos e, em seguida, inovar nesses padrões permite que nos tornemos cada vez melhores ao longo de nossa vida. Esta capacidade de aprendizagem e capacidade de evolução está relacionada a um conceito chamado [plasticidade cerebral](https://www.simplypsychology.org/brain-plasticity.html). Superficialmente, podemos traçar algumas semelhanças motivacionais entre o processo de aprendizado do cérebro humano e os conceitos de aprendizado de máquina. - -O [cérebro humano](https://www.livescience.com/29365-human-brain.html) percebe coisas do mundo real, processa as informações percebidas, toma decisões racionais e executa certas ações com base nas circunstâncias. Isso é o que chamamos de comportamento inteligente. Quando programamos um fac-símile do processo comportamental inteligente para uma máquina, isso é chamado de inteligência artificial (AI). - -Embora os termos possam ser confundidos, o machine learning (ML) é um subconjunto importante da inteligência artificial. **ML se preocupa em usar algoritmos especializados para descobrir informações significativas e encontrar padrões ocultos de dados percebidos para corroborar o processo de tomada de decisão racional**. - -![AI, ML, deep learning, data science](../images/ai-ml-ds.png) - -> Um diagrama que mostra as relações entre AI, ML, deep learning, and data science. Infográfico de [Jen Looper](https://twitter.com/jenlooper) inspirado [neste gráfico](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining) - -## O que você aprenderá neste curso - -Nesta seção, vamos cobrir apenas os conceitos básicos de machine learning que um iniciante deve conhecer. Abordamos o que chamamos de 'machine learning clássico' principalmente usando o Scikit-learn, uma excelente biblioteca que muitos alunos usam para aprender o básico. Para compreender conceitos mais amplos de inteligência artificial ou deep learning, é indispensável um forte conhecimento fundamental de machine learning e, por isso, gostaríamos de oferecê-lo aqui. - -Neste curso você aprenderá: - -- conceitos fundamentais de machine learning -- a história do ML -- ML e justiça -- técnicas de regressão de ML -- técnicas de classificação com ML -- técnicas de agrupamento de ML -- técnicas de processamento de linguagem natural de ML -- técnicas de ML de previsão de séries temporais -- aprendizagem por reforço -- aplicativos do mundo real para ML - -## O que não cobriremos - -- deep learning -- redes neurais (neural networks) -- AI - -Para tornar essa experiência de aprendizado melhor, evitaremos as complexidades das redes neurais, 'deep learning' - construção de modelos em várias camadas usando redes neurais - e AI, que discutiremos em um currículo diferente. Também ofereceremos um futuro currículo de ciência de dados para consolidar esse aspecto desse campo mais amplo. - -## Por que estudar machine learning? - -O machine learning, de uma perspectiva de sistemas, é definido como a criação de sistemas automatizados que podem aprender padrões ocultos de dados para ajudar na tomada de decisões inteligentes. - -Essa motivação é vagamente inspirada em como o cérebro humano aprende certas coisas com base nos dados que percebe do mundo exterior. - -✅ Pense por um minuto por que uma empresa iria querer tentar usar estratégias de machine learning em vez de criar um mecanismo baseado em regras embutido. - -### Aplicações do machine learning - -Os aplicativos de machine learning agora estão em quase todos os lugares e são tão onipresentes quanto os dados que fluem em nossas sociedades, gerados por nossos smartphones, dispositivos conectados e outros sistemas. Considerando o imenso potencial dos algoritmos de aprendizado de máquina (machine learning) de última geração, os pesquisadores têm explorado sua capacidade de resolver problemas multidimensionais e multidisciplinares da vida real com excelentes resultados positivos. - -**Você pode usar o machine learning de várias maneiras**: - -- Para prever a probabilidade de doença a partir do histórico médico ou relatórios de um paciente. -- Para aproveitar os dados meteorológicos para prever eventos meteorológicos. -- Para entender o sentimento de um texto. -- Para detectar notícias falsas (fake news) e impedir a propagação de propaganda. - -Finanças, economia, ciências da terra, exploração espacial, engenharia biomédica, ciências cognitivas e até mesmo campos das humanidades adaptaram o machine learning para resolver os árduos e pesados problemas de processamento de dados de seu domínio. - -O machine learning automatiza o processo de descoberta de padrões, encontrando insights significativos do mundo real ou dados gerados. Ele provou ser altamente valioso em aplicações comerciais, de saúde e financeiras, entre outras. - -Em um futuro próximo, compreender os fundamentos do machine learning será uma obrigação para pessoas de qualquer domínio devido à sua ampla adoção. - ---- - -## 🚀 Desafio - -Esboce, no papel ou usando um aplicativo online como [Excalidraw](https://excalidraw.com/), sua compreensão das diferenças entre AI, ML, deep learning e data science. Adicione algumas idéias de problemas que cada uma dessas técnicas é boa para resolver. - -## [Questionário pós-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2?loc=ptbr) - -## Revisão e autoestudo - -Para saber mais sobre como você pode trabalhar com algoritmos de ML na nuvem, siga este [Caminho de aprendizagem](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott). - -Faça o [Caminho de aprendizagem](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) sobre os fundamentos do ML. - -## Tarefa - -[Comece a trabalhar](assignment.pt-br.md) diff --git a/1-Introduction/1-intro-to-ML/translations/README.ru.md b/1-Introduction/1-intro-to-ML/translations/README.ru.md deleted file mode 100644 index e7a19915..00000000 --- a/1-Introduction/1-intro-to-ML/translations/README.ru.md +++ /dev/null @@ -1,149 +0,0 @@ -# Введение в машинное обучение - - - -[![ML, AI, глубокое обучение - в чем разница?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, глубокое обучение - в чем разница?") - -> 🎥 Нажмите на изображение выше, чтобы просмотреть видео, в котором обсуждается разница между машинным обучением, искусственным интеллектом и глубоким обучением. - -## [Тест перед лекцией](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/) - ---- - -Добро пожаловать на курс классического машинного обучения для начинающих! Если вы новичок в этой теме или опытный специалист по машинному обучению, желающий освежить свои знания в какой-либо области, мы рады, что вы присоединились к нам! Мы хотим создать удобную стартовую площадку для вашего изучения машинного обучения и будем рады ответить и учесть ваши [отзывы](https://github.com/microsoft/ML-For-Beginners/discussions). - -[![Введение в ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Введение в ML") - -> 🎥 Нажмите на изображение выше, чтобы просмотреть видео: Джон Гуттаг из Массачусетского технологического института представляет машинное обучение - ---- -## Начало работы с машинным обучением - -Перед тем, как приступить к изучению этой учебной программы, вам необходимо настроить компьютер и подготовить его для работы с ноутбуками локально. - -- **Настройте свою машину с помощью этих видео**. Воспользуйтесь следующими ссылками, чтобы узнать [как установить Python](https://youtu.be/CXZYvNRIAKM) в вашей системе и [настроить текстовый редактор](https://youtu.be/EU8eayHWoZg) для разработки. -- **Изучите Python**. Также рекомендуется иметь базовые знания о [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), языке программирования, полезном для специалистов по данным, который мы используем в этом курсе. -- **Изучите Node.js и JavaScript**. Мы также несколько раз используем JavaScript в этом курсе при создании веб-приложений, поэтому вам потребуется установить [node](https://nodejs.org) и [npm](https://www.npmjs.com/), а также [Visual Studio Code](https://code.visualstudio.com/), доступный для разработки как на Python, так и на JavaScript. -- **Создайте учетную запись GitHub**. Поскольку вы нашли нас на [GitHub](https://github.com), возможно, у вас уже есть учетная запись, но если нет, создайте ее, а затем создайте форк этой учебной программы, чтобы использовать ее самостоятельно. (Не стесняйтесь поставить звезду этому репозиторию 😊) -- **Ознакомьтесь со Scikit-learn**. Ознакомьтесь со [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), набором библиотек для машинного обучения, на которые мы ссылаемся в этих уроках. - ---- -## Что такое машинное обучение? - -Термин "машинное обучение" - один из самых популярных и часто используемых сегодня терминов. Очень вероятно, что вы слышали этот термин хотя бы раз, если вы хоть немного знакомы с технологиями, независимо от того, в какой области вы работаете. Однако механика машинного обучения остается загадкой для большинства людей. Для новичка в машинном обучении эта тема иногда может показаться сложной. Поэтому важно понимать, что такое машинное обучение на самом деле, и изучать его шаг за шагом на практических примерах. - ---- -## Кривая хайпа - -![кривая хайпа ML](../images/hype.png) - -> Google Trends показывает недавнюю "кривую хайпа" термина "машинное обучение". - ---- -## Загадочная вселенная - -Мы живем во вселенной, полной завораживающих загадок. Великие ученые, такие как Стивен Хокинг, Альберт Эйнштейн и многие другие, посвятили свою жизнь поиску значимой информации, раскрывающей тайны окружающего нас мира. Это условие обучения: ребенок из года в год узнает новое и раскрывает структуру окружающего мира по мере взросления. - ---- -## Мозг ребенка - -Мозг и органы чувств ребенка воспринимают факты из своего окружения и постепенно изучают скрытые закономерности жизни, которые помогают ребенку выработать логические правила для определения усвоенных закономерностей. Процесс обучения человеческого мозга делает людей самыми изощренными живыми существами в этом мире. Постоянное обучение, обнаружение скрытых закономерностей и последующее внедрение инноваций, позволяет нам становиться лучше и лучше на протяжении всей жизни. Эта способность к обучению и способность к развитию связаны с концепцией, называемой [пластичность мозга](https://www.simplypsychology.org/brain-plasticity.html). На первый взгляд, мы можем выявить некоторые мотивационные сходства между процессом обучения человеческого мозга и концепциями машинного обучения. - ---- -## Человеческий мозг - -[Человеческий мозг](https://www.livescience.com/29365-human-brain.html) воспринимает вещи из реального мира, обрабатывает воспринимаемую информацию, принимает рациональные решения и выполняет определенные действия в зависимости от обстоятельств. Это то, что мы называем разумным поведением. Когда мы программируем копию интеллектуального поведенческого процесса на компьютере, это называется искусственным интеллектом (ИИ). - ---- -## Немного терминологии - -Хотя термины могут запутать, машинное обучение (ML) является важным подмножеством искусственного интеллекта. **Машинное обучение занимается использованием специализированных алгоритмов для раскрытия значимой информации и поиска скрытых закономерностей из воспринимаемых данных для подтверждения рационального процесса принятия решений**. - ---- -## AI, ML, глубокое обучение - -![AI, ML, глубокое обучение, наука о данных](../images/ai-ml-ds.png) - -> Диаграмма, показывающая взаимосвязь между ИИ, машинным обучением, глубоким обучением и наукой о данных. Инфографика [Jen Looper](https://twitter.com/jenlooper), вдохновленная [этим рисунком](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining) - ---- -## Концепции, которые охватывает этот курс - -В этой учебной программе мы собираемся охватить только основные концепции машинного обучения, которые должен знать новичок. Мы рассматриваем то, что мы называем «классическим машинным обучением», в первую очередь с использованием Scikit-learn, отличной библиотеки, которую многие студенты используют для изучения основ. Чтобы понять более широкие концепции искусственного интеллекта или глубокого обучения, необходимы сильные фундаментальные знания о машинном обучении, и поэтому мы хотели бы предложить их здесь. - ---- -## В этом курсе вы узнаете: - -- основные концепции машинного обучения -- история ML -- ML и равнодоступность -- методы регрессионного машинного обучения -- классификация методов машинного обучения -- методы кластеризации машинного обучения -- методы машинного обучения обработки естественного языка -- методы машинного обучения прогнозирования временных рядов -- обучение с подкреплением -- реальные приложения для машинного обучения - ---- -## Что мы не будем рассказывать - -- глубокое обучение -- нейронные сети -- AI - -Чтобы улучшить процесс изучения, мы будем избегать сложностей нейронных сетей, «глубокого обучения» - многоуровневого построения моделей с использованием нейронных сетей - и искусственного интеллекта, которые мы обсудим в другой учебной программе. Мы также представим учебную программу по науке о данных, чтобы сосредоточиться на этом аспекте этой более широкой области. - ---- -## Зачем изучать машинное обучение? - -Машинное обучение с системной точки зрения определяется как создание автоматизированных систем, которые могут изучать скрытые закономерности из данных, чтобы помочь в принятии разумных решений. - -Эта мотивация во многом основана на том, как человеческий мозг учится определенным вещам на основе данных, которые он воспринимает из внешнего мира. - -✅ Задумайтесь на минутку, почему компания может попытаться использовать стратегии машинного обучения вместо создания жестко запрограммированного механизма на основе правил. - ---- -## Приложения машинного обучения - -Приложения машинного обучения сейчас есть почти повсюду, и они столь же повсеместны, как и данные, которые присутствующие в нашем обществе, генерируемые нашими смартфонами, подключенными к сети устройствами и другими системами. Учитывая огромный потенциал современных алгоритмов машинного обучения, исследователи изучали их способность решать многомерные и междисциплинарные проблемы реальной жизни с отличными положительными результатами. - ---- -## Примеры применяемого ML - -**Машинное обучение можно использовать разными способами**: - -- Предсказать вероятность заболевания на основании истории болезни пациента или отчетов. -- Использование данных о погоде для прогнозирования погодных явлений. -- Чтобы понять тональность текста. -- Для обнаружения фейковых новостей, чтобы остановить распространение пропаганды. - -Финансы, экономика, науки о Земле, освоение космоса, биомедицинская инженерия, когнитивистика и даже области гуманитарных наук адаптировали машинное обучение для решения сложных задач обработки данных в своей области. - ---- -## Заключение - -Машинное обучение автоматизирует процесс обнаружения шаблонов, находя важные закономерности из реальных или сгенерированных данных. Оно зарекомендовало себя, среди прочего, как очень ценный инструмент для бизнеса, здравоохранения и финансов. - -В ближайшем будущем понимание основ машинного обучения станет обязательным для людей из любой области из-за его широкого распространения. - ---- -# 🚀 Вызов - -Набросайте на бумаге или с помощью онлайн-приложения, такого как [Excalidraw](https://excalidraw.com/), ваше понимание различий между AI, ML, глубоким обучением и наукой о данных. Добавьте несколько идей о проблемах, которые может решить каждый из этих методов. - -# [Тест после лекции](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/) - ---- -# Обзор и самообучение - -Чтобы узнать больше о том, как вы можете работать с алгоритмами машинного обучения в облаке, следуйте курсу [Learning Path](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott). - -Пройдите курс [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-77952-leestott) по основам машинного обучения. - ---- -# Задание - -[Подготовьте среду разработки](assignment.ru.md) diff --git a/1-Introduction/1-intro-to-ML/translations/README.tr.md b/1-Introduction/1-intro-to-ML/translations/README.tr.md deleted file mode 100644 index 79744c52..00000000 --- a/1-Introduction/1-intro-to-ML/translations/README.tr.md +++ /dev/null @@ -1,114 +0,0 @@ -# Makine Öğrenimine Giriş - -[![ML, AI, Derin öğrenme - Farkları nelerdir?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, Derin öğrenme - Farkları nelerdir?") - -> 🎥 Makine öğrenimi, yapay zeka ve derin öğrenme arasındaki farkı tartışan bir video için yukarıdaki resme tıklayın. - -## [Ders öncesi sınav](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1?loc=tr) - -### Introduction - -Yeni başlayanlar için klasik makine öğrenimi üzerine olan bu kursa hoş geldiniz! İster bu konuda tamamen yeni olun, ister belli bir alandaki bilgilerini tazelemek isteyen deneyimli bir makine öğrenimi uygulayıcısı olun, aramıza katılmanızdan mutluluk duyarız! Makine öğrenimi çalışmanız için samimi bir başlangıç ​​noktası oluşturmak istiyoruz ve [geri bildiriminizi](https://github.com/microsoft/ML-For-Beginners/discussions) değerlendirmekten, yanıtlamaktan ve hayata geçirmekten memnuniyet duyarız. - -[![Makine Öğrenimine Giriş](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Makine Öğrenimine Giriş") - -> 🎥 Video için yukarıdaki resme tıklayın: MIT'den John Guttag, makine öğrenimini tanıtıyor -### Makine Öğrenimine Başlamak - -Bu müfredata başlamadan önce, bilgisayarınızın yerel olarak (Jupyter) not defterlerini çalıştırmak için hazır olması gerekir. - -- **Makinenizi bu videolar rehberliğinde yapılandırın**. Bu [video setinde](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6) makinenizi nasıl kuracağınız hakkında daha fazla bilgi edinin. -- **Python öğrenin**. Ayrıca, veri bilimciler için faydalı bir programlama dili olan ve bu derslerde kullandığımız [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) programlama dili hakkında temel bilgilere sahip olmanız da önerilir. -- **Node.js ve JavaScript'i öğrenin**. Web uygulamaları oluştururken de bu kursta JavaScript'i birkaç kez kullanıyoruz, bu nedenle [node](https://nodejs.org), [npm](https://www.npmjs.com/) ve ayrıca hem Python hem de JavaScript geliştirme için kullanılabilen [Visual Studio Code](https://code.visualstudio.com/) yüklü olmalıdır. -- **GitHub hesabı oluşturun**. Bizi burada [GitHub](https://github.com) üzerinde bulduğunuza göre, zaten bir hesabınız olabilir, ancak mevcut değilse, bir tane hesap oluşturun ve ardından bu müfredatı kendi başınıza kullanmak için çatallayın (fork). (Bize de yıldız vermekten çekinmeyin 😊) -- **Scikit-learn'ü keşfedin**. Bu derslerde referans verdiğimiz, bir dizi ML kütüphanesinden oluşan [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) hakkında bilgi edinin. - -### Makine öğrenimi nedir? - -'Makine öğrenimi' terimi, günümüzün en popüler ve sık kullanılan terimlerinden biridir. Hangi alanda çalışırsanız çalışın, teknolojiyle ilgili bir tür aşinalığınız varsa, bu terimi en az bir kez duymuş olma ihtimaliniz yüksektir. Bununla birlikte, makine öğreniminin mekanikleri, yani çalışma prensipleri, çoğu insan için bir gizemdir. Makine öğrenimine yeni başlayan biri için konu bazen bunaltıcı gelebilir. Bu nedenle, makine öğreniminin gerçekte ne olduğunu anlamak ve pratik örnekler üzerinden adım adım öğrenmek önemlidir. - -![ML heyecan eğrisi](../images/hype.png) - -> Google Trendler, 'makine öğrenimi' teriminin son 'heyecan eğrisini' gösteriyor - -Büyüleyici gizemlerle dolu bir evrende yaşıyoruz. Stephen Hawking, Albert Einstein ve daha pek çoğu gibi büyük bilim adamları, hayatlarını çevremizdeki dünyanın gizemlerini ortaya çıkaran anlamlı bilgiler aramaya adadılar. Öğrenmenin insani yönü de budur: insan evladı yeni şeyler öğrenir ve yetişkinliğe doğru büyüdükçe her yıl kendi dünyasının yapısını ortaya çıkarır. - -Bir çocuğun beyni ve duyuları, çevrelerindeki gerçekleri algılar ve çocuğun, öğrenilen kalıpları tanımlamak için mantıksal kurallar oluşturmasına yardımcı olan gizli yaşam kalıplarını yavaş yavaş öğrenir. İnsan beyninin öğrenme süreci, insanı bu dünyanın en gelişmiş canlısı yapar. Gizli kalıpları keşfederek sürekli öğrenmek ve sonra bu kalıplar üzerinde yenilik yapmak, yaşamımız boyunca kendimizi giderek daha iyi hale getirmemizi sağlar. Bu öğrenme kapasitesi ve gelişen kabiliyet, [beyin plastisitesi](https://www.simplypsychology.org/brain-plasticity.html) adı verilen bir kavramla ilgilidir. Yüzeysel olarak, insan beyninin öğrenme süreci ile makine öğrenimi kavramları arasında bazı motivasyonel benzerlikler çizebiliriz. - -[İnsan beyni](https://www.livescience.com/29365-human-brain.html) gerçek dünyadaki şeyleri algılar, algılanan bilgileri işler, mantıksal kararlar verir ve koşullara göre belirli eylemler gerçekleştirir. Akıllıca davranmak dediğimiz şey buydu işte. Bir makineye akıllı davranış sürecinin bir kopyasını programladığımızda buna yapay zeka (İngilizce haliyle artificial intelligence, kısaca **AI**) denir. - -Terimler karıştırılabilse de, makine öğrenimi (İngilizce haliyle machine learning, kısaca **ML**), yapay zekanın önemli bir alt kümesidir. **ML, mantıklı karar verme sürecini desteklemek için anlamlı bilgileri ortaya çıkarmak ve algılanan verilerden gizli kalıpları bulmak için özel algoritmalar kullanmakla ilgilenir**. - -![AI, ML, derin öğrenme, veri bilimi](../images/ai-ml-ds.png) - -> Yapay zeka, makine öğrenimi, derin öğrenme ve veri bilimi arasındaki ilişkileri gösteren bir diyagram. Bu infografik, [şu grafikten](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-) ilham alan [Jen Looper](https://twitter.com/jenlooper) tarafından hazırlanmıştır. - -> AI (Artificial Intelligence): Yapay zekâ -> ML(Machine Learning): Makine öğrenimi -> Deep Learning: Derin Öğrenme -> Data Science: Veri bilimi - -## Bu kursta neler öğreneceksiniz - -Bu müfredatta, yalnızca yeni başlayanların bilmesi gereken makine öğreniminin temel kavramlarını ele alacağız. 'Klasik makine öğrenimi' dediğimiz şeyi, öncelikle birçok öğrencinin temel bilgileri öğrenmek için kullandığı mükemmel bir kütüphane olan Scikit-learn'ü kullanarak ele alıyoruz. Daha geniş yapay zeka veya derin öğrenme kavramlarını anlamak için, güçlü bir temel makine öğrenimi bilgisi vazgeçilmezdir ve bu yüzden onu burada sunmak istiyoruz. - -Bu kursta şunları öğreneceksiniz: - -- makine öğreniminin temel kavramları -- ML'nin tarihi -- ML ve adillik -- regresyon ML teknikleri -- sınıflandırma ML teknikleri -- kümeleme ML teknikleri -- doğal dil işleme ML teknikleri -- zaman serisi tahmini ML teknikleri -- pekiştirmeli öğrenme -- ML için gerçek-dünya uygulamaları - -## Neyi kapsamayacağız - -- derin öğrenme -- sinir ağları -- yapay zeka - -Daha iyi bir öğrenme deneyimi sağlamak için, farklı bir müfredatta tartışacağımız sinir ağları, 'derin öğrenme' (sinir ağlarını kullanarak çok katmanlı modeller oluşturma) ve yapay zekânın karmaşıklıklarından kaçınacağız. Ayrıca, bu daha geniş alanın bu yönüne odaklanmak için yakında çıkacak bir veri bilimi müfredatı sunacağız. - -## Neden makine öğrenimi üzerinde çalışmalısınız? - -Sistemler perspektifinden makine öğrenimi, akıllı kararlar almaya yardımcı olmak için verilerden gizli kalıpları öğrenebilen otomatik sistemlerin oluşturulması olarak tanımlanır. - -Bu motivasyon, insan beyninin dış dünyadan algıladığı verilere dayanarak belirli şeyleri nasıl öğrendiğinden bir miktar esinlenmiştir. - -✅ Bir işletmenin, sabit kurallara dayalı bir karar aracı oluşturmak yerine neden makine öğrenimi stratejilerini kullanmayı denemek isteyebileceklerini bir an için düşünün. - -### Makine öğrenimi uygulamaları - -Makine öğrenimi uygulamaları artık neredeyse her yerde ve akıllı telefonlarımız, internete bağlı cihazlarımız ve diğer sistemlerimiz tarafından üretilen, toplumlarımızda akan veriler kadar yaygın hale gelmiş durumda. Son teknoloji makine öğrenimi algoritmalarının muazzam potansiyelini göz önünde bulunduran araştırmacılar, bu algoritmaların çok boyutlu ve çok disiplinli gerçek hayat problemlerini çözme yeteneklerini araştırıyorlar ve oldukça olumlu sonuçlar alıyorlar. - -**Makine öğrenimini birçok şekilde kullanabilirsiniz**: - -- Bir hastanın tıbbi geçmişinden veya raporlarından hastalık olasılığını tahmin etmek -- Hava olaylarını tahmin etmek için hava durumu verilerini kullanmak -- Bir metnin duygu durumunu anlamak -- Propagandanın yayılmasını durdurmak için sahte haberleri tespit etmek - -Finans, ekonomi, yer bilimi, uzay araştırmaları, biyomedikal mühendislik, bilişsel bilim ve hatta beşeri bilimlerdeki alanlar, kendi alanlarının zorlu ve ağır veri işleme sorunlarını çözmek için makine öğrenimini tekniklerini kullanmaya başladılar. - -Makine öğrenimi, gerçek dünyadan veya oluşturulan verilerden anlamlı içgörüler bularak örüntü bulma sürecini otomatikleştirir. Diğerlerinin yanı sıra iş, sağlık ve finansal uygulamalarda son derece değerli olduğunu kanıtlamıştır. - -Yakın gelecekte, yaygın olarak benimsenmesi nedeniyle makine öğreniminin temellerini anlamak, tüm alanlardan insanlar için bir zorunluluk olacak. - ---- -## 🚀 Meydan Okuma - -Kağıt üzerinde veya [Excalidraw](https://excalidraw.com/) gibi çevrimiçi bir uygulama kullanarak AI, makine öğrenimi, derin öğrenme ve veri bilimi arasındaki farkları anladığınızdan emin olun. Bu tekniklerin her birinin çözmede iyi olduğu bazı problem fikirleri ekleyin. - -## [Ders sonrası test](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2?loc=tr) - -## İnceleme ve Bireysel Çalışma - -Bulutta makine öğrenimi algoritmalarıyla nasıl çalışabileceğiniz hakkında daha fazla bilgi edinmek için bu [Eğitim Patikasını](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott) izleyin. - -## Ödev - -[Haydi başlayalım!](assignment.tr.md) \ No newline at end of file diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md deleted file mode 100644 index e919ff02..00000000 --- a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md +++ /dev/null @@ -1,107 +0,0 @@ -# 机器学习介绍 - -[![机器学习，人工智能，深度学习-有什么区别?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "机器学习，人工智能，深度学习-有什么区别?") - -> 🎥 点击上面的图片观看讨论机器学习、人工智能和深度学习之间区别的视频。 - -## [课前测验](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/) - -### 介绍 - -欢迎来到这个经典机器学习的初学者课程！无论你是这个主题的新手，还是一个有经验的 ML 从业者，我们都很高兴你能加入我们！我们希望为你的 ML 研究创建一个好的开始，并很乐意评估、回应和接受你的[反馈](https://github.com/microsoft/ML-For-Beginners/discussions)。 - -[![机器学习简介](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML") - -> 🎥 单击上图观看视频：麻省理工学院的 John Guttag 介绍机器学习 -### 机器学习入门 - -在开始本课程之前，你需要设置计算机能在本地运行 Jupyter Notebooks。 - -- **按照这些视频里的讲解配置你的计算机**。了解有关如何在此[视频集](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)中设置计算机的更多信息。 -- **学习 Python**。 还建议你对 [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) 有一个基本的了解。这是我们在本课程中使用的一种对数据科学家有用的编程语言。 -- **学习 Node.js 和 JavaScript**。在本课程中，我们在构建 web 应用程序时也使用过几次 JavaScript，因此你需要有 [Node.js](https://nodejs.org) 和 [npm](https://www.npmjs.com/) 以及 [Visual Studio Code](https://code.visualstudio.com/) 用于 Python 和 JavaScript 开发。 -- **创建 GitHub 帐户**。既然你在 [GitHub](https://github.com) 上找到我们，你可能已经有了一个帐户，但如果没有，请创建一个帐户，然后 fork 此课程自己使用(也给我们一颗星星吧😊) -- **探索 Scikit-learn**. 熟悉 [Scikit-learn]([https://scikit-learn.org/stable/user_guide.html)，我们在这些课程中引用的一组 ML 库。 - -### 什么是机器学习？ - -术语“机器学习”是当今最流行和最常用的术语之一。 如果你对科技有某种程度的熟悉，那么很可能你至少听说过这个术语一次，无论你在哪个领域工作。然而，机器学习的机制对大多数人来说是一个谜。 对于机器学习初学者来说，这个主题有时会让人感到不知所措。 因此，了解机器学习的实质是什么，并通过实例一步一步地了解机器学习是很重要的。 - -![机器学习趋势曲线](../images/hype.png) - -> 谷歌趋势显示了“机器学习”一词最近的“趋势曲线” - -我们生活在一个充满迷人奥秘的宇宙中。像史蒂芬·霍金、阿尔伯特·爱因斯坦等伟大的科学家，以及更多的人，都致力于寻找有意义的信息，揭示我们周围世界的奥秘。这就是人类学习的条件：一个人类的孩子在长大成人的过程中，一年又一年地学习新事物并揭示世界的结构。 - -孩子的大脑和感官感知到周围的事实，并逐渐学习隐藏的生活模式，这有助于孩子制定逻辑规则来识别学习模式。人类大脑的学习过程使人类成为世界上最复杂的生物。不断地学习，通过发现隐藏的模式，然后对这些模式进行创新，使我们能够使自己在一生中变得越来越好。这种学习能力和进化能力与一个叫做[大脑可塑性](https://www.simplypsychology.org/brain-plasticity.html)的概念有关。从表面上看，我们可以在人脑的学习过程和机器学习的概念之间找到一些动机上的相似之处。 - -[人脑](https://www.livescience.com/29365-human-brain.html) 从现实世界中感知事物，处理感知到的信息，做出理性的决定，并根据环境执行某些行动。这就是我们所说的智能行为。当我们将智能行为过程的复制品编程到计算机上时，它被称为人工智能 (AI)。 - -尽管这些术语可能会混淆，但机器学习 (ML) 是人工智能的一个重要子集。 **机器学习关注使用专门的算法来发现有意义的信息，并从感知数据中找到隐藏的模式，以证实理性的决策过程**。 - -![人工智能、机器学习、深度学习、数据科学](../images/ai-ml-ds.png) - -> 显示 AI、ML、深度学习和数据科学之间关系的图表。图片作者 [Jen Looper](https://twitter.com/jenlooper)，灵感来自[这张图](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining) - -## 你将在本课程中学到什么 - -在本课程中，我们将仅涵盖初学者必须了解的机器学习的核心概念。 我们主要使用 Scikit-learn 来介绍我们所谓的“经典机器学习”，这是一个许多学生用来学习基础知识的优秀库。要理解更广泛的人工智能或深度学习的概念，机器学习的基础知识是必不可少的，所以我们想在这里提供它。 - -在本课程中，你将学习： - -- 机器学习的核心概念 -- 机器学习的历史 -- 机器学习和公平性 -- 回归 -- 分类 -- 聚类 -- 自然语言处理 -- 时序预测 -- 强化学习 -- 机器学习的实际应用 -## 我们不会涵盖的内容 - -- 深度学习 -- 神经网络 -- AI - -为了获得更好的学习体验，我们将避免神经网络、“深度学习”（使用神经网络的多层模型构建）和人工智能的复杂性，我们将在不同的课程中讨论这些问题。 我们还将提供即将推出的数据科学课程，以专注于这个更大领域的这一方面。 -## 为什么要学习机器学习？ - -从系统的角度来看，机器学习被定义为创建可以从数据中学习隐藏模式以帮助做出智能决策的自动化系统。 - -这种动机大致是受人脑如何根据它从外部世界感知到的数据来学习某些东西的启发。 - -✅ 想一想为什么企业想要尝试使用机器学习策略而不是创建基于硬编码的规则引擎。 - -### 机器学习的应用 - -机器学习的应用现在几乎无处不在，就像我们的智能手机、互联设备和其他系统产生的数据一样无处不在。考虑到最先进的机器学习算法的巨大潜力，研究人员一直在探索其解决多维多学科现实问题的能力，并取得了巨大的积极成果。 - -**你可以在很多方面使用机器学习**: - -- 根据病人的病史或报告来预测患病的可能性。 -- 利用天气数据预测天气。 -- 理解文本的情感。 -- 检测假新闻以阻止其传播。 - -金融、经济学、地球科学、太空探索、生物医学工程、认知科学，甚至人文学科领域都采用机器学习来解决其领域中艰巨的、数据处理繁重的问题。 - -机器学习通过从真实世界或生成的数据中发现有意义的见解，自动化了模式发现的过程。事实证明，它在商业、健康和金融应用等方面具有很高的价值。 - -在不久的将来，由于机器学习的广泛应用，了解机器学习的基础知识将成为任何领域的人们的必修课。 - ---- -## 🚀 挑战 - -在纸上或使用 [Excalidraw](https://excalidraw.com/) 等在线应用程序绘制草图，了解你对 AI、ML、深度学习和数据科学之间差异的理解。添加一些关于这些技术擅长解决的问题的想法。 - -## [阅读后测验](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/) - -## 复习与自学 - -要了解有关如何在云中使用 ML 算法的更多信息，请遵循以下[学习路径](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott)。 - -## 任务 - -[启动并运行](assignment.zh-cn.md) diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md b/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md deleted file mode 100644 index bac84df3..00000000 --- a/1-Introduction/1-intro-to-ML/translations/README.zh-tw.md +++ /dev/null @@ -1,103 +0,0 @@ -# 機器學習介紹 - -[![機器學習，人工智能，深度學習-有什麽區別?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "機器學習，人工智能，深度學習-有什麽區別?") - -> 🎥 點擊上面的圖片觀看討論機器學習、人工智能和深度學習之間區別的視頻。 -## [課前測驗](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/1/) - -### 介紹 - -歡迎來到這個經典機器學習的初學者課程！無論你是這個主題的新手，還是一個有經驗的 ML 從業者，我們都很高興你能加入我們！我們希望為你的 ML 研究創建一個好的開始，並很樂意評估、回應和接受你的[反饋](https://github.com/microsoft/ML-For-Beginners/discussions)。 - -[![機器學習簡介](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML") - -> 🎥 單擊上圖觀看視頻：麻省理工學院的 John Guttag 介紹機器學習 -### 機器學習入門 -在開始本課程之前，你需要設置計算機能在本地運行 Jupyter Notebooks。 - -- **按照這些視頻裏的講解配置你的計算機**。了解有關如何在此[視頻集](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)中設置計算機的更多信息。 -- **學習 Python**。 還建議你對 [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) 有一個基本的了解。這是我們在本課程中使用的一種對數據科學家有用的編程語言。 -- **學習 Node.js 和 JavaScript**。在本課程中，我們在構建 web 應用程序時也使用過幾次 JavaScript，因此你需要有 [Node.js](https://nodejs.org) 和 [npm](https://www.npmjs.com/) 以及 [Visual Studio Code](https://code.visualstudio.com/) 用於 Python 和 JavaScript 開發。 -- **創建 GitHub 帳戶**。既然你在 [GitHub](https://github.com) 上找到我們，你可能已經有了一個帳戶，但如果沒有，請創建一個帳戶，然後 fork 此課程自己使用(也給我們一顆星星吧😊) -- **探索 Scikit-learn**. 熟悉 [Scikit-learn]([https://scikit-learn.org/stable/user_guide.html)，我們在這些課程中引用的一組 ML 庫。 - -### 什麽是機器學習？ - -術語「機器學習」是當今最流行和最常用的術語之一。 如果你對科技有某種程度的熟悉，那麽很可能你至少聽說過這個術語一次，無論你在哪個領域工作。然而，機器學習的機製對大多數人來說是一個謎。 對於機器學習初學者來說，這個主題有時會讓人感到不知所措。 因此，了解機器學習的實質是什麽，並通過實例一步一步地了解機器學習是很重要的。 - -![機器學習趨勢曲線](../images/hype.png) - -> 谷歌趨勢顯示了「機器學習」一詞最近的「趨勢曲線」 -我們生活在一個充滿迷人奧秘的宇宙中。像史蒂芬·霍金、阿爾伯特·愛因斯坦等偉大的科學家，以及更多的人，都致力於尋找有意義的信息，揭示我們周圍世界的奧秘。這就是人類學習的條件：一個人類的孩子在長大成人的過程中，一年又一年地學習新事物並揭示世界的結構。 - -孩子的大腦和感官感知到周圍的事實，並逐漸學習隱藏的生活模式，這有助於孩子製定邏輯規則來識別學習模式。人類大腦的學習過程使人類成為世界上最復雜的生物。不斷地學習，通過發現隱藏的模式，然後對這些模式進行創新，使我們能夠使自己在一生中變得越來越好。這種學習能力和進化能力與一個叫做[大腦可塑性](https://www.simplypsychology.org/brain-plasticity.html)的概念有關。從表面上看，我們可以在人腦的學習過程和機器學習的概念之間找到一些動機上的相似之處。 - -[人腦](https://www.livescience.com/29365-human-brain.html) 從現實世界中感知事物，處理感知到的信息，做出理性的決定，並根據環境執行某些行動。這就是我們所說的智能行為。當我們將智能行為過程的復製品編程到計算機上時，它被稱為人工智能 (AI)。 - -盡管這些術語可能會混淆，但機器學習 (ML) 是人工智能的一個重要子集。 **機器學習關註使用專門的算法來發現有意義的信息，並從感知數據中找到隱藏的模式，以證實理性的決策過程**。 - -![人工智能、機器學習、深度學習、數據科學](../images/ai-ml-ds.png) - -> 顯示 AI、ML、深度學習和數據科學之間關系的圖表。圖片作者 [Jen Looper](https://twitter.com/jenlooper)，靈感來自[這張圖](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining) -## 你將在本課程中學到什麽 - -在本課程中，我們將僅涵蓋初學者必須了解的機器學習的核心概念。 我們主要使用 Scikit-learn 來介紹我們所謂的「經典機器學習」，這是一個許多學生用來學習基礎知識的優秀庫。要理解更廣泛的人工智能或深度學習的概念，機器學習的基礎知識是必不可少的，所以我們想在這裏提供它。 - -在本課程中，你將學習： - -- 機器學習的核心概念 -- 機器學習的歷史 -- 機器學習和公平性 -- 回歸 -- 分類 -- 聚類 -- 自然語言處理 -- 時序預測 -- 強化學習 -- 機器學習的實際應用 -## 我們不會涵蓋的內容 - -- 深度學習 -- 神經網絡 -- AI - -為了獲得更好的學習體驗，我們將避免神經網絡、「深度學習」（使用神經網絡的多層模型構建）和人工智能的復雜性，我們將在不同的課程中討論這些問題。 我們還將提供即將推出的數據科學課程，以專註於這個更大領域的這一方面。 -## 為什麽要學習機器學習？ - -從系統的角度來看，機器學習被定義為創建可以從數據中學習隱藏模式以幫助做出智能決策的自動化系統。 - -這種動機大致是受人腦如何根據它從外部世界感知到的數據來學習某些東西的啟發。 - -✅ 想一想為什麽企業想要嘗試使用機器學習策略而不是創建基於硬編碼的規則引擎。 - -### 機器學習的應用 - -機器學習的應用現在幾乎無處不在，就像我們的智能手機、互聯設備和其他系統產生的數據一樣無處不在。考慮到最先進的機器學習算法的巨大潛力，研究人員一直在探索其解決多維多學科現實問題的能力，並取得了巨大的積極成果。 - -**你可以在很多方面使用機器學習**: - -- 根據病人的病史或報告來預測患病的可能性。 -- 利用天氣數據預測天氣。 -- 理解文本的情感。 -- 檢測假新聞以阻止其傳播。 - -金融、經濟學、地球科學、太空探索、生物醫學工程、認知科學，甚至人文學科領域都采用機器學習來解決其領域中艱巨的、數據處理繁重的問題。 - -機器學習通過從真實世界或生成的數據中發現有意義的見解，自動化了模式發現的過程。事實證明，它在商業、健康和金融應用等方面具有很高的價值。 - -在不久的將來，由於機器學習的廣泛應用，了解機器學習的基礎知識將成為任何領域的人們的必修課。 - ---- -## 🚀 挑戰 - -在紙上或使用 [Excalidraw](https://excalidraw.com/) 等在線應用程序繪製草圖，了解你對 AI、ML、深度學習和數據科學之間差異的理解。添加一些關於這些技術擅長解決的問題的想法。 - -## [閱讀後測驗](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/2/) - -## 復習與自學 - -要了解有關如何在雲中使用 ML 算法的更多信息，請遵循以下[學習路徑](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-77952-leestott)。 - -## 任務 - -[啟動並運行](assignment.zh-tw.md) \ No newline at end of file diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.es.md b/1-Introduction/1-intro-to-ML/translations/assignment.es.md deleted file mode 100644 index 5b428135..00000000 --- a/1-Introduction/1-intro-to-ML/translations/assignment.es.md +++ /dev/null @@ -1,9 +0,0 @@ -# Lévantate y corre - -## Instrucciones - -En esta tarea no calificada, debe repasar Python y hacer que su entorno esté en funcionamiento y sea capaz de ejecutar cuadernos. - -Tome esta [Ruta de aprendizaje de Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), y luego configure sus sistemas con estos videos introductorios: - -https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.fr.md b/1-Introduction/1-intro-to-ML/translations/assignment.fr.md deleted file mode 100644 index b8513048..00000000 --- a/1-Introduction/1-intro-to-ML/translations/assignment.fr.md +++ /dev/null @@ -1,10 +0,0 @@ -# Être opérationnel - - -## Instructions - -Dans ce devoir non noté, vous devez vous familiariser avec Python et rendre votre environnement opérationnel et capable d'exécuter des notebook. - -Suivez ce [parcours d'apprentissage Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), puis configurez votre système en parcourant ces vidéos introductives : - -https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.id.md b/1-Introduction/1-intro-to-ML/translations/assignment.id.md deleted file mode 100644 index a22848f4..00000000 --- a/1-Introduction/1-intro-to-ML/translations/assignment.id.md +++ /dev/null @@ -1,9 +0,0 @@ -# Persiapan - -## Instruksi - -Dalam tugas yang tidak dinilai ini, kamu akan mempelajari Python dan mempersiapkan *environment* kamu sehingga dapat digunakan untuk menjalankan *notebook*. - -Ambil [Jalur Belajar Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) ini, kemudian persiapkan sistem kamu dengan menonton video-video pengantar ini: - -https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.it.md b/1-Introduction/1-intro-to-ML/translations/assignment.it.md deleted file mode 100644 index 15c41f29..00000000 --- a/1-Introduction/1-intro-to-ML/translations/assignment.it.md +++ /dev/null @@ -1,9 +0,0 @@ -# Tempi di apprendimento brevi - -## Istruzioni - -In questo compito senza valutazione, si dovrebbe rispolverare Python e rendere il proprio ambiente attivo e funzionante, in grado di eseguire notebook. - -Si segua questo [percorso di apprendimento di Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) e quindi si configurino i propri sistemi seguendo questi video introduttivi: - -https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.ja.md b/1-Introduction/1-intro-to-ML/translations/assignment.ja.md deleted file mode 100644 index 4c427561..00000000 --- a/1-Introduction/1-intro-to-ML/translations/assignment.ja.md +++ /dev/null @@ -1,9 +0,0 @@ -# 稼働させる - -## 指示 - -この評価のない課題では、Pythonについて復習し、環境を稼働させてノートブックを実行できるようにする必要があります。 - -この[Pythonラーニングパス](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)を受講し、次の入門用ビデオに従ってシステムをセットアップしてください。 - -https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.ko.md b/1-Introduction/1-intro-to-ML/translations/assignment.ko.md deleted file mode 100644 index 2b8e72a8..00000000 --- a/1-Introduction/1-intro-to-ML/translations/assignment.ko.md +++ /dev/null @@ -1,9 +0,0 @@ -# 시작해 봅시다 - -## 설명 - -이 미채점 과제에서는 파이썬(Python)을 복습하고 Python 실행 환경 설정 및 노트북(Jupyter Notebook) 실행 방법까지 숙지해 보시길 바랍니다. - -다음 [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)를 이수하시고, 아래 Python 입문 강좌를 통해 Python 설치 및 실행 환경을 설정해 보세요: - -https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.pt-br.md b/1-Introduction/1-intro-to-ML/translations/assignment.pt-br.md deleted file mode 100644 index 983d930f..00000000 --- a/1-Introduction/1-intro-to-ML/translations/assignment.pt-br.md +++ /dev/null @@ -1,9 +0,0 @@ -# Comece a Trabalhar - -## Instruções - -Nesta tarefa não corrigida, você deve se aprimorar em Python e colocar seu ambiente em funcionamento e capaz de executar notebooks. - -Faça o [Caminho de aprendizagem do Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), e, em seguida, faça a configuração de seus sistemas analisando estes vídeos introdutórios: - -https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.ru.md b/1-Introduction/1-intro-to-ML/translations/assignment.ru.md deleted file mode 100644 index 8a06eedb..00000000 --- a/1-Introduction/1-intro-to-ML/translations/assignment.ru.md +++ /dev/null @@ -1,9 +0,0 @@ -# Настройте среду разработки - -## Инструкции - -Это задание не оценивается. Вы должны освежить в памяти Python и настроить свою среду, чтобы она могла запускать ноутбуки. - -Воспользуйтесь этим курсом [Python Learning Path](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott), а затем настройте свою систему, просмотрев эти вводные видео: - -https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.tr.md b/1-Introduction/1-intro-to-ML/translations/assignment.tr.md deleted file mode 100644 index ed70c424..00000000 --- a/1-Introduction/1-intro-to-ML/translations/assignment.tr.md +++ /dev/null @@ -1,9 +0,0 @@ -# Haydi Başlayalım - -## Talimatlar - -Bu not-verilmeyen ödevde, Python bilgilerinizi tazelemeli, geliştirme ortamınızı çalışır duruma getirmeli ve not defterlerini çalıştırabilmelisiniz. - -Bu [Python Eğitim Patikasını](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott) bitirin ve ardından bu tanıtım videolarını izleyerek sistem kurulumunuzu yapın : - -https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 \ No newline at end of file diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md deleted file mode 100644 index 9aa0dd28..00000000 --- a/1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md +++ /dev/null @@ -1,9 +0,0 @@ -# 启动和运行 - -## 说明 - -在这个不评分的作业中，你应该温习一下 Python，将 Python 环境能够运行起来，并且可以运行 notebooks。 - -学习这个 [Python 学习路径](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)，然后通过这些介绍性的视频将你的系统环境设置好： - -https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.zh-tw.md b/1-Introduction/1-intro-to-ML/translations/assignment.zh-tw.md deleted file mode 100644 index fa913b28..00000000 --- a/1-Introduction/1-intro-to-ML/translations/assignment.zh-tw.md +++ /dev/null @@ -1,9 +0,0 @@ -# 啟動和運行 - -## 說明 - -在這個不評分的作業中，你應該溫習一下 Python，將 Python 環境能夠運行起來，並且可以運行 notebooks。 - -學習這個 [Python 學習路徑](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-77952-leestott)，然後通過這些介紹性的視頻將你的系統環境設置好： - -https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6 diff --git a/1-Introduction/2-history-of-ML/README.md b/1-Introduction/2-history-of-ML/README.md deleted file mode 100644 index 48e519e1..00000000 --- a/1-Introduction/2-history-of-ML/README.md +++ /dev/null @@ -1,150 +0,0 @@ -# History of machine learning - -![Summary of History of machine learning in a sketchnote](../../sketchnotes/ml-history.png) -> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac) - -## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/) - ---- - -[![ML for beginners - History of Machine Learning](https://img.youtube.com/vi/N6wxM4wZ7V0/0.jpg)](https://youtu.be/N6wxM4wZ7V0 "ML for beginners - History of Machine Learning") - -> 🎥 Click the image above for a short video working through this lesson. - -In this lesson, we will walk through the major milestones in the history of machine learning and artificial intelligence. - -The history of artificial intelligence (AI) as a field is intertwined with the history of machine learning, as the algorithms and computational advances that underpin ML fed into the development of AI. It is useful to remember that, while these fields as distinct areas of inquiry began to crystallize in the 1950s, important [algorithmic, statistical, mathematical, computational and technical discoveries](https://wikipedia.org/wiki/Timeline_of_machine_learning) predated and overlapped this era. In fact, people have been thinking about these questions for [hundreds of years](https://wikipedia.org/wiki/History_of_artificial_intelligence): this article discusses the historical intellectual underpinnings of the idea of a 'thinking machine.' - ---- -## Notable discoveries - -- 1763, 1812 [Bayes Theorem](https://wikipedia.org/wiki/Bayes%27_theorem) and its predecessors. This theorem and its applications underlie inference, describing the probability of an event occurring based on prior knowledge. -- 1805 [Least Square Theory](https://wikipedia.org/wiki/Least_squares) by French mathematician Adrien-Marie Legendre. This theory, which you will learn about in our Regression unit, helps in data fitting. -- 1913 [Markov Chains](https://wikipedia.org/wiki/Markov_chain), named after Russian mathematician Andrey Markov, is used to describe a sequence of possible events based on a previous state. -- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) is a type of linear classifier invented by American psychologist Frank Rosenblatt that underlies advances in deep learning. - ---- - -- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) is an algorithm originally designed to map routes. In an ML context it is used to detect patterns. -- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) is used to train [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network). -- 1982 [Recurrent Neural Networks](https://wikipedia.org/wiki/Recurrent_neural_network) are artificial neural networks derived from feedforward neural networks that create temporal graphs. - -✅ Do a little research. What other dates stand out as pivotal in the history of ML and AI? - ---- -## 1950: Machines that think - -Alan Turing, a truly remarkable person who was voted [by the public in 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) as the greatest scientist of the 20th century, is credited as helping to lay the foundation for the concept of a 'machine that can think.' He grappled with naysayers and his own need for empirical evidence of this concept in part by creating the [Turing Test](https://www.bbc.com/news/technology-18475646), which you will explore in our NLP lessons. - ---- -## 1956: Dartmouth Summer Research Project - -"The Dartmouth Summer Research Project on artificial intelligence was a seminal event for artificial intelligence as a field," and it was here that the term 'artificial intelligence' was coined ([source](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)). - -> Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. - ---- - -The lead researcher, mathematics professor John McCarthy, hoped "to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." The participants included another luminary in the field, Marvin Minsky. - -The workshop is credited with having initiated and encouraged several discussions including "the rise of symbolic methods, systems focussed on limited domains (early expert systems), and deductive systems versus inductive systems." ([source](https://wikipedia.org/wiki/Dartmouth_workshop)). - ---- -## 1956 - 1974: "The golden years" - -From the 1950s through the mid '70s, optimism ran high in the hope that AI could solve many problems. In 1967, Marvin Minsky stated confidently that "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall) - -natural language processing research flourished, search was refined and made more powerful, and the concept of 'micro-worlds' was created, where simple tasks were completed using plain language instructions. - ---- - -Research was well funded by government agencies, advances were made in computation and algorithms, and prototypes of intelligent machines were built. Some of these machines include: - -* [Shakey the robot](https://wikipedia.org/wiki/Shakey_the_robot), who could maneuver and decide how to perform tasks 'intelligently'. - - ![Shakey, an intelligent robot](images/shakey.jpg) - > Shakey in 1972 - ---- - -* Eliza, an early 'chatterbot', could converse with people and act as a primitive 'therapist'. You'll learn more about Eliza in the NLP lessons. - - ![Eliza, a bot](images/eliza.png) - > A version of Eliza, a chatbot - ---- - -* "Blocks world" was an example of a micro-world where blocks could be stacked and sorted, and experiments in teaching machines to make decisions could be tested. Advances built with libraries such as [SHRDLU](https://wikipedia.org/wiki/SHRDLU) helped propel language processing forward. - - [![blocks world with SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world with SHRDLU") - - > 🎥 Click the image above for a video: Blocks world with SHRDLU - ---- -## 1974 - 1980: "AI Winter" - -By the mid 1970s, it had become apparent that the complexity of making 'intelligent machines' had been understated and that its promise, given the available compute power, had been overblown. Funding dried up and confidence in the field slowed. Some issues that impacted confidence included: ---- -- **Limitations**. Compute power was too limited. -- **Combinatorial explosion**. The amount of parameters needed to be trained grew exponentially as more was asked of computers, without a parallel evolution of compute power and capability. -- **Paucity of data**. There was a paucity of data that hindered the process of testing, developing, and refining algorithms. -- **Are we asking the right questions?**. The very questions that were being asked began to be questioned. Researchers began to field criticism about their approaches: - - Turing tests came into question by means, among other ideas, of the 'chinese room theory' which posited that, "programming a digital computer may make it appear to understand language but could not produce real understanding." ([source](https://plato.stanford.edu/entries/chinese-room/)) - - The ethics of introducing artificial intelligences such as the "therapist" ELIZA into society was challenged. - ---- - -At the same time, various AI schools of thought began to form. A dichotomy was established between ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies) practices. _Scruffy_ labs tweaked programs for hours until they had the desired results. _Neat_ labs "focused on logic and formal problem solving". ELIZA and SHRDLU were well-known _scruffy_ systems. In the 1980s, as demand emerged to make ML systems reproducible, the _neat_ approach gradually took the forefront as its results are more explainable. - ---- -## 1980s Expert systems - -As the field grew, its benefit to business became clearer, and in the 1980s so did the proliferation of 'expert systems'. "Expert systems were among the first truly successful forms of artificial intelligence (AI) software." ([source](https://wikipedia.org/wiki/Expert_system)). - -This type of system is actually _hybrid_, consisting partially of a rules engine defining business requirements, and an inference engine that leveraged the rules system to deduce new facts. - -This era also saw increasing attention paid to neural networks. - ---- -## 1987 - 1993: AI 'Chill' - -The proliferation of specialized expert systems hardware had the unfortunate effect of becoming too specialized. The rise of personal computers also competed with these large, specialized, centralized systems. The democratization of computing had begun, and it eventually paved the way for the modern explosion of big data. - ---- -## 1993 - 2011 - -This epoch saw a new era for ML and AI to be able to solve some of the problems that had been caused earlier by the lack of data and compute power. The amount of data began to rapidly increase and become more widely available, for better and for worse, especially with the advent of the smartphone around 2007. Compute power expanded exponentially, and algorithms evolved alongside. The field began to gain maturity as the freewheeling days of the past began to crystallize into a true discipline. - ---- -## Now - -Today machine learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)). - ---- - -It remains to be seen what the future holds, but it is important to understand these computer systems and the software and algorithms that they run. We hope that this curriculum will help you to gain a better understanding so that you can decide for yourself. - -[![The history of deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning") -> 🎥 Click the image above for a video: Yann LeCun discusses the history of deep learning in this lecture - ---- -## 🚀Challenge - -Dig into one of these historical moments and learn more about the people behind them. There are fascinating characters, and no scientific discovery was ever created in a cultural vacuum. What do you discover? - -## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/) - ---- -## Review & Self Study - -Here are items to watch and listen to: - -[This podcast where Amy Boyd discusses the evolution of AI](http://runasradio.com/Shows/Show/739) - -[![The history of AI by Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd") - ---- - -## Assignment - -[Create a timeline](assignment.md) diff --git a/1-Introduction/2-history-of-ML/assignment.md b/1-Introduction/2-history-of-ML/assignment.md deleted file mode 100644 index a12e85a7..00000000 --- a/1-Introduction/2-history-of-ML/assignment.md +++ /dev/null @@ -1,11 +0,0 @@ -# Create a timeline - -## Instructions - -Using [this repo](https://github.com/Digital-Humanities-Toolkit/timeline-builder), create a timeline of some aspect of the history of algorithms, mathematics, statistics, AI, or ML, or a combination of these. You can focus on one person, one idea, or a long timespan of thought. 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100755 index 116cc7e4..00000000 --- a/1-Introduction/2-history-of-ML/translations/README.es.md +++ /dev/null @@ -1,117 +0,0 @@ -# Historia del machine learning - -![Resumen de la historia del machine learning en un boceto](../../sketchnotes/ml-history.png) -> Boceto por [Tomomi Imura](https://www.twitter.com/girlie_mac) - -## [Cuestionario previo a la conferencia](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3?loc=es) - -En esta lección, analizaremos los principales hitos en la historia del machine learning y la inteligencia artificial. - -La historia de la inteligencia artificial (AI) como campo está entrelazada con la historia del machine learning, ya que los algoritmos y avances computacionales que sustentan el ML ayudaron al desarrollo de la inteligencia artificial. Es útil recordar que, si bien estos campos comenzaron a cristalizar en la década de 1950 como áreas distintas de investigación, importantes [descubrimientos algorítmicos, estadísticos, matemáticos, computacionales y técnicos](https://wikipedia.org/wiki/Timeline_of_machine_learning) fueron predecesores y contemporáneos a esta era. De hecho, las personas han estado pensando en estas preguntas durante [cientos de años](https://wikipedia.org/wiki/History_of_artificial_intelligence): este artículo analiza los fundamentos intelectuales históricos de la idea de una 'máquina pensante.' - -## Descubrimientos notables - -- 1763, 1812 [Teorema de Bayes](https://es.wikipedia.org/wiki/Teorema_de_Bayes) y sus predecesores. Este teorema y sus aplicaciones son la base de la inferencia, describiendo la probabilidad de que ocurra un evento basado en el conocimiento previo. -- 1805 [Teoría de mínimos cuadrados](https://es.wikipedia.org/wiki/M%C3%ADnimos_cuadrados) por el matemático francés Adrien-Marie Legendre. Esta teoría, sobre la que aprenderemos en nuestra unidad de Regresión, ayuda al ajustar los modelos a los datos. -- 1913 [Cadenas de Markov](https://es.wikipedia.org/wiki/Cadena_de_M%C3%A1rkov) el nombre del matemático ruso Andrey Markov es utilizado para describir una secuencia de posibles eventos basados en su estado anterior. -- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) es un tipo de clasificador lineal inventado por el psicólogo Frank Rosenblatt que subyace a los avances en el deep learning. -- 1967 [Nearest Neighbor (Vecino más cercano)](https://es.wikipedia.org/wiki/K_vecinos_m%C3%A1s_pr%C3%B3ximos) es un algoritmo diseñado originalmente para trazar rutas. En un contexto de ML, se utiliza para detectar patrones. -- 1970 [Retropropagación](https://es.wikipedia.org/wiki/Propagaci%C3%B3n_hacia_atr%C3%A1s): es usada para entrenar [redes neuronales prealimentadas](https://es.wikipedia.org/wiki/Red_neuronal_prealimentada). -- 1982 [Redes neuronales recurrentes](https://es.wikipedia.org/wiki/Red_neuronal_recurrente) son redes neuronales artificiales derivadas de redes neuronales prealimentadas que crean grafos temporales. - -✅ Investigue un poco. ¿Qué otras fechas se destacan como fundamentales en la historia del machine learning (ML) y la inteligencia artificial (AI)? -## 1950: Máquinas que piensan - -Alan Turing, una persona verdaderamente notable que fue votada [por el público en 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) como el científico más grande del siglo XX, a quien se le atribuye haber ayudado a sentar las bases del concepto de una 'máquina que puede pensar.' Lidió con los detractores y con su propia necesidad de evidencia empírica de este concepto en parte mediante la creación de la [prueba de Turing](https://www.bbc.com/news/technology-18475646), que explorarás en nuestras lecciones de procesamiento de lenguaje natural (NLP, por sus siglas en inglés). - -## 1956: Dartmouth Summer Research Project - -"The Dartmouth Summer Research Project sobre inteligencia artificial fue un evento fundamental para la inteligencia artificial como campo" y fue aquí donde se acuñó el término 'inteligencia artificial' ([fuente](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)). - - -> Todos los aspectos del aprendizaje y cualquier otra característica de la inteligencia pueden, en principio, describirse con tanta precisión que se puede hacer una máquina para simularlos. - -El investigador principal, el profesor de matemáticas John McCarthy, esperaba "proceder sobre las bases de la conjetura de que cada aspecto del aprendizaje o cualquier otra característica de la inteligencia pueden, en principio, describirse con tanta precisión que se puede hacer una máquina para simularlos." Los participantes, incluyeron otro gran experto en el campo, Marvin Minsky. - -El taller tiene el mérito de haber iniciado y alentado varias discusiones que incluyen "el surgimiento de métodos simbólicos, sistemas en dominios limitados (primeros sistemas expertos), y sistemas deductivos contra sistemas inductivos." ([fuente](https://es.wikipedia.org/wiki/Conferencia_de_Dartmouth)). - -## 1956 - 1974: "Los años dorados" - -Desde la década de 1950, hasta mediados de la de 1970, el optimismo se elevó con la esperanza de que la AI pudiera resolver muchos problemas. En 1967, Marvin Minsky declaró con seguridad que "dentro de una generación ... el problema de crear 'inteligencia artificial' estará resuelto en gran medida." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall) - -La investigación del procesamiento del lenguaje natural floreció, la búsqueda se refinó y se hizo más poderosa, y el concepto de 'micro-mundos' fue creado, donde se completaban tareas simples utilizando instrucciones en lenguaje sencillo. - -La investigación estuvo bien financiada por agencias gubernamentales, se realizaron avances en computación y algoritmos, y se construyeron prototipos de máquinas inteligentes. Algunas de esta máquinas incluyen: - -* [Shakey la robot](https://wikipedia.org/wiki/Shakey_the_robot), que podría maniobrar y decidir cómo realizar las tareas de forma 'inteligente'. - - ![Shakey, un robot inteligente](images/shakey.jpg) - > Shakey en 1972 - -* Eliza, unas de las primeras 'chatterbot', podía conversar con las personas y actuar como un 'terapeuta' primitivo. Aprenderá más sobre Eliza en las lecciones de NLP. - - ![Eliza, un bot](images/eliza.png) - > Una versión de Eliza, un chatbot - -* "Blocks world" era un ejemplo de micro-world donde los bloques se podían apilar y ordenar, y se podían probar experimentos en máquinas de enseñanza para tomar decisiones. Los avances creados con librerías como [SHRDLU](https://wikipedia.org/wiki/SHRDLU) ayudaron a inpulsar el procesamiento del lenguaje natural. - - [![blocks world con SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world con SHRDLU") - - > 🎥 Haga clic en la imagen de arriba para ver un video: Blocks world con SHRDLU - -## 1974 - 1980: "Invierno de la AI" - -A mediados de la década de 1970, se hizo evidente que la complejidad de la fabricación de 'máquinas inteligentes' se había subestimado y que su promesa, dado la potencia computacional disponible, había sido exagerada. La financiación se agotó y la confianza en el campo se ralentizó. Algunos problemas que impactaron la confianza incluyeron: - -- **Limitaciones**. La potencia computacional era demasiado limitada. -- **Explosión combinatoria**. La cantidad de parámetros necesitados para entrenar creció exponencialmente a medida que se pedía más a las computadoras sin una evolución paralela de la potencia y la capacidad de cómputo. -- **Escasez de datos**. Hubo una escasez de datos que obstaculizó el proceso de pruebas, desarrollo y refinamiento de algoritmos. -- **¿Estamos haciendo las preguntas correctas?**. Las mismas preguntas que se estaban formulando comenzaron a cuestionarse. Los investigadores comenzaron a criticar sus métodos: - - Las pruebas de Turing se cuestionaron por medio, entre otras ideas, de la 'teoría de la habitación china' que postulaba que "programar una computadora digital puede hacerse que parezca que entiende el lenguaje, pero no puede producir una comprensión real" ([fuente](https://plato.stanford.edu/entries/chinese-room/)) - - Se cuestionó la ética de introducir inteligencias artificiales como la "terapeuta" Eliza en la sociedad. - -Al mismo tiempo, comenzaron a formarse varias escuelas de pensamiento de AI. Se estableció una dicotomía entre las prácticas ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). _Scruffy_ labs modificó los programas durante horas hasta que obtuvieron los objetivos deseados. _Neat_ labs "centrados en la lógica y la resolución de problemas formales". ELIZA y SHRDLU eran sistemas _scruffy_ muy conocidos. En la década de 1980, cuando surgió la demanda para hacer que los sistemas de aprendizaje fueran reproducibles, el enfoque _neat_ gradualmente tomó la vanguardia a medida que sus resultados eran más explicables. - -## Systemas expertos de la década de 1980 - -A medida que el campo creció, su beneficio para las empresas se hizo más claro, y en la década de 1980 también lo hizo la proliferación de 'sistemas expertos'. "Los sistemas expertos estuvieron entre las primeras formas verdaderamente exitosas de software de inteligencia artificial (IA)." ([fuente](https://wikipedia.org/wiki/Expert_system)). - -Este tipo de sistemas es en realidad _híbrido_, que consta parcialmente de un motor de reglas que define los requisitos comerciales, y un motor de inferencia que aprovechó el sistema de reglas para deducir nuevos hechos. - -En esta era también se prestó mayor atención a las redes neuronales. - -## 1987 - 1993: AI 'Chill' - -La proliferación de hardware de sistemas expertos especializados tuvo el desafortunado efecto de volverse demasiado especializado. El auge de las computadoras personales también compitió con estos grandes sistemas centralizados especializados. La democratización de la informática había comenzado, y finalmente, allanó el camino para la explosión moderna del big data. - -## 1993 - 2011 - -Esta época vió una nueva era para el ML y la IA para poder resolver problemas que anteriormente provenían de la falta de datos y de poder de cómputo. La cantidad de datos comenzó a aumentar rápidamente y a estar más disponible, para bien o para mal, especialmente con la llegada del smartphone alrededor del 2007. El poder computacional se expandió exponencialmente y los algoritmos evolucionaron al mismo tiempo. El campo comenzó a ganar madurez a medida que los días libres del pasado comenzaron a cristalizar en una verdadera disciplina. - -## Ahora - -Hoy en día, machine learning y la inteligencia artificial tocan casi todos los aspectos de nuestras vidas. Esta era requiere de una comprensión cuidadosa de los riesgos y los efectos potenciales de estos algoritmos en las vidas humanas. Como ha dicho Brad Smith de Microsoft, "La tecnología de la información plantea problemas que van al corazón de las protecciones fundamentales de los derechos humanos, como la privacidad y la libertad de expresión. Esos problemas aumentan las responsabilidades de las empresas de tecnología que crean estos productos. En nuestra opinión, también exige regulación gubernamental reflexiva y el desarrollo de normas sobre usos aceptables" ([fuente](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)). - -Queda por ver qué depara el futuro, pero es importante entender estos sistemas informáticos y el software y los algoritmos que ejecutan. Esperamos que este plan de estudios le ayude a comprender mejor para que pueda decidir por si mismo. - -[![La historia del deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning") -> 🎥 Haga clic en la imagen de arriba para ver un video: Yann LeCun analiza la historia del deep learning en esta conferencia - ---- -## 🚀Desafío - -Sumérjase dentro de unos de estos momentos históricos y aprenda más sobre las personas detrás de ellos. Hay personajes fascinantes y nunca ocurrió ningún descubrimiento científico en un vacío cultural. ¿Qué descubres? - -## [Cuestionario posterior a la lección](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4?loc=es) - -## Revisión y autoestudio - -Aquí hay elementos para ver y escuchar: - -[Este podcast donde Amy Boyd habla sobre la evolución de la IA](http://runasradio.com/Shows/Show/739) - -[![La historia de la IA por Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "La historia de la IA por Amy Boyd") - -## Tarea - -[Crea un timeline](assignment.md) diff --git a/1-Introduction/2-history-of-ML/translations/README.fr.md b/1-Introduction/2-history-of-ML/translations/README.fr.md deleted file mode 100644 index d9ecad4f..00000000 --- a/1-Introduction/2-history-of-ML/translations/README.fr.md +++ /dev/null @@ -1,117 +0,0 @@ -# Histoire du Machine Learning (apprentissage automatique) - -![Résumé de l'histoire du machine learning dans un sketchnote](../../../sketchnotes/ml-history.png) -> Sketchnote de [Tomomi Imura](https://www.twitter.com/girlie_mac) - -## [Quizz préalable](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3?loc=fr) - -Dans cette leçon, nous allons parcourir les principales étapes de l'histoire du machine learning et de l'intelligence artificielle. - -L'histoire de l'intelligence artificielle, l'IA, en tant que domaine est étroitement liée à l'histoire du machine learning, car les algorithmes et les avancées informatiques qui sous-tendent le ML alimentent le développement de l'IA. Bien que ces domaines en tant que domaines de recherches distincts ont commencé à se cristalliser dans les années 1950, il est important de rappeler que les [découvertes algorithmiques, statistiques, mathématiques, informatiques et techniques](https://wikipedia.org/wiki/Timeline_of_machine_learning) ont précédé et chevauchait cette époque. En fait, le monde réfléchit à ces questions depuis [des centaines d'années](https://fr.wikipedia.org/wiki/Histoire_de_l%27intelligence_artificielle) : cet article traite des fondements intellectuels historiques de l'idée d'une « machine qui pense ». - -## Découvertes notables - -- 1763, 1812 [théorème de Bayes](https://wikipedia.org/wiki/Bayes%27_theorem) et ses prédécesseurs. Ce théorème et ses applications sous-tendent l'inférence, décrivant la probabilité qu'un événement se produise sur la base de connaissances antérieures. -- 1805 [Théorie des moindres carrés](https://wikipedia.org/wiki/Least_squares) par le mathématicien français Adrien-Marie Legendre. Cette théorie, que vous découvrirez dans notre unité Régression, aide à l'ajustement des données. -- 1913 [Chaînes de Markov](https://wikipedia.org/wiki/Markov_chain) du nom du mathématicien russe Andrey Markov sont utilisées pour décrire une séquence d'événements possibles basée sur un état antérieur. -- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) est un type de classificateur linéaire inventé par le psychologue américain Frank Rosenblatt qui sous-tend les progrès de l'apprentissage en profondeur. -- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) est un algorithme conçu à l'origine pour cartographier les itinéraires. Dans un contexte ML, il est utilisé pour détecter des modèles. -- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) est utilisé pour former des [réseaux de neurones feedforward (propagation avant)](https://fr.wikipedia.org/wiki/R%C3%A9seau_de_neurones_%C3%A0_propagation_avant). -- 1982 [Réseaux de neurones récurrents](https://wikipedia.org/wiki/Recurrent_neural_network) sont des réseaux de neurones artificiels dérivés de réseaux de neurones à réaction qui créent des graphes temporels. - -✅ Faites une petite recherche. Quelles autres dates sont marquantes dans l'histoire du ML et de l'IA ? - -## 1950 : Des machines qui pensent - -Alan Turing, une personne vraiment remarquable qui a été élue [par le public en 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) comme le plus grand scientifique du 20e siècle, est reconnu pour avoir aidé à jeter les bases du concept d'une "machine qui peut penser". Il a lutté avec ses opposants et son propre besoin de preuves empiriques de sa théorie en créant le [Test de Turing] (https://www.bbc.com/news/technology-18475646), que vous explorerez dans nos leçons de NLP (TALN en français). - -## 1956 : Projet de recherche d'été à Dartmouth - -« Le projet de recherche d'été de Dartmouth sur l'intelligence artificielle a été un événement fondateur pour l'intelligence artificielle en tant que domaine », et c'est ici que le terme « intelligence artificielle » a été inventé ([source](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)). - -> Chaque aspect de l'apprentissage ou toute autre caractéristique de l'intelligence peut en principe être décrit si précisément qu'une machine peut être conçue pour les simuler. - -Le chercheur en tête, le professeur de mathématiques John McCarthy, espérait « procéder sur la base de la conjecture selon laquelle chaque aspect de l'apprentissage ou toute autre caractéristique de l'intelligence peut en principe être décrit avec une telle précision qu'une machine peut être conçue pour les simuler ». Les participants comprenaient une autre sommité dans le domaine, Marvin Minsky. - -L'atelier est crédité d'avoir initié et encouragé plusieurs discussions, notamment « l'essor des méthodes symboliques, des systèmes spécialisés sur des domaines limités (premiers systèmes experts) et des systèmes déductifs par rapport aux systèmes inductifs ». ([source](https://fr.wikipedia.org/wiki/Conf%C3%A9rence_de_Dartmouth)). - -## 1956 - 1974 : "Les années d'or" - -Des années 50 au milieu des années 70, l'optimisme était au rendez-vous en espérant que l'IA puisse résoudre de nombreux problèmes. En 1967, Marvin Minsky a déclaré avec assurance que « Dans une génération... le problème de la création d'"intelligence artificielle" sera substantiellement résolu. » (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall) - -La recherche sur le Natural Language Processing (traitement du langage naturel en français) a prospéré, la recherche a été affinée et rendue plus puissante, et le concept de « micro-mondes » a été créé, où des tâches simples ont été effectuées en utilisant des instructions en langue naturelle. - -La recherche a été bien financée par les agences gouvernementales, des progrès ont été réalisés dans le calcul et les algorithmes, et des prototypes de machines intelligentes ont été construits. Certaines de ces machines incluent : - -* [Shakey le robot](https://fr.wikipedia.org/wiki/Shakey_le_robot), qui pouvait manœuvrer et décider comment effectuer des tâches « intelligemment ». - - ![Shakey, un robot intelligent](../images/shakey.jpg) - > Shaky en 1972 - -* Eliza, une des premières « chatbot », pouvait converser avec les gens et agir comme une « thérapeute » primitive. Vous en apprendrez plus sur Eliza dans les leçons de NLP. - - ![Eliza, un bot](../images/eliza.png) - > Une version d'Eliza, un chatbot - -* Le « monde des blocs » était un exemple de micro-monde où les blocs pouvaient être empilés et triés, et où des expériences d'apprentissages sur des machines, dans le but qu'elles prennent des décisions, pouvaient être testées. Les avancées réalisées avec des bibliothèques telles que [SHRDLU](https://fr.wikipedia.org/wiki/SHRDLU) ont contribué à faire avancer le natural language processing. - - [![Monde de blocs avec SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "Monde de blocs avec SHRDLU" ) - - > 🎥 Cliquez sur l'image ci-dessus pour une vidéo : Blocks world with SHRDLU - -## 1974 - 1980 : « l'hiver de l'IA » - -Au milieu des années 1970, il était devenu évident que la complexité de la fabrication de « machines intelligentes » avait été sous-estimée et que sa promesse, compte tenu de la puissance de calcul disponible, avait été exagérée. Les financements se sont taris et la confiance dans le domaine s'est ralentie. Parmi les problèmes qui ont eu un impact sur la confiance, citons : - -- **Restrictions**. La puissance de calcul était trop limitée. -- **Explosion combinatoire**. Le nombre de paramètres à former augmentait de façon exponentielle à mesure que l'on en demandait davantage aux ordinateurs, sans évolution parallèle de la puissance et de la capacité de calcul. -- **Pénurie de données**. Il y avait un manque de données qui a entravé le processus de test, de développement et de raffinement des algorithmes. -- **Posions-nous les bonnes questions ?**. Les questions mêmes, qui étaient posées, ont commencé à être remises en question. Les chercheurs ont commencé à émettre des critiques sur leurs approches : - - Les tests de Turing ont été remis en question au moyen, entre autres, de la « théorie de la chambre chinoise » qui postulait que « la programmation d'un ordinateur numérique peut faire croire qu'il comprend le langage mais ne peut pas produire une compréhension réelle ». ([source](https://plato.stanford.edu/entries/chinese-room/)) - - L'éthique de l'introduction d'intelligences artificielles telles que la "thérapeute" ELIZA dans la société a été remise en cause. - -Dans le même temps, diverses écoles de pensée sur l'IA ont commencé à se former. Une dichotomie a été établie entre les pratiques IA ["scruffy" et "neat"](https://wikipedia.org/wiki/Neats_and_scruffies). Les laboratoires _Scruffy_ peaufinaient leurs programmes pendant des heures jusqu'à ce qu'ils obtiennent les résultats souhaités. Les laboratoires _Neat_ "se concentraient sur la logique et la résolution formelle de problèmes". ELIZA et SHRDLU étaient des systèmes _scruffy_ bien connus. Dans les années 1980, alors qu'émergeait la demande de rendre les systèmes ML reproductibles, l'approche _neat_ a progressivement pris le devant de la scène car ses résultats sont plus explicables. - -## 1980 : Systèmes experts - -Au fur et à mesure que le domaine s'est développé, ses avantages pour les entreprises sont devenus plus clairs, particulièrement via les « systèmes experts » dans les années 1980. "Les systèmes experts ont été parmi les premières formes vraiment réussies de logiciels d'intelligence artificielle (IA)." ([source](https://fr.wikipedia.org/wiki/Syst%C3%A8me_expert)). - -Ce type de système est en fait _hybride_, composé en partie d'un moteur de règles définissant les exigences métier et d'un moteur d'inférence qui exploite le système de règles pour déduire de nouveaux faits. - -Cette époque a également vu une attention croissante accordée aux réseaux de neurones. - -## 1987 - 1993 : IA « Chill » - -La prolifération du matériel spécialisé des systèmes experts a eu pour effet malheureux de devenir trop spécialisée. L'essor des ordinateurs personnels a également concurrencé ces grands systèmes spécialisés et centralisés. La démocratisation de l'informatique a commencé et a finalement ouvert la voie à l'explosion des mégadonnées. - -## 1993 - 2011 - -Cette époque a vu naître une nouvelle ère pour le ML et l'IA afin de résoudre certains des problèmes qui n'avaient pu l'être plus tôt par le manque de données et de puissance de calcul. La quantité de données a commencé à augmenter rapidement et à devenir plus largement disponibles, pour le meilleur et pour le pire, en particulier avec l'avènement du smartphone vers 2007. La puissance de calcul a augmenté de façon exponentielle et les algorithmes ont évolué parallèlement. Le domaine a commencé à gagner en maturité alors que l'ingéniosité a commencé à se cristalliser en une véritable discipline. - -## À présent - -Aujourd'hui, le machine learning et l'IA touchent presque tous les aspects de notre vie. Cette ère nécessite une compréhension approfondie des risques et des effets potentiels de ces algorithmes sur les vies humaines. Comme l'a déclaré Brad Smith de Microsoft, « les technologies de l'information soulèvent des problèmes qui vont au cœur des protections fondamentales des droits de l'homme comme la vie privée et la liberté d'expression. Ces problèmes accroissent la responsabilité des entreprises technologiques qui créent ces produits. À notre avis, ils appellent également à une réglementation gouvernementale réfléchie et au développement de normes autour des utilisations acceptables" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)). - -Reste à savoir ce que l'avenir nous réserve, mais il est important de comprendre ces systèmes informatiques ainsi que les logiciels et algorithmes qu'ils exécutent. Nous espérons que ce programme vous aidera à mieux les comprendre afin que vous puissiez décider par vous-même. - -[![L'histoire du Deep Learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "L'histoire du Deep Learning") -> 🎥 Cliquez sur l'image ci-dessus pour une vidéo : Yann LeCun discute de l'histoire du deep learning dans cette conférence - ---- -## 🚀Challenge - -Plongez dans l'un de ces moments historiques et apprenez-en plus sur les personnes derrière ceux-ci. Il y a des personnalités fascinantes, et aucune découverte scientifique n'a jamais été créée avec un vide culturel. Que découvrez-vous ? - -## [Quiz de validation des connaissances](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4?loc=fr) - -## Révision et auto-apprentissage - -Voici quelques articles à regarder et à écouter : - -[Ce podcast où Amy Boyd discute de l'évolution de l'IA](http://runasradio.com/Shows/Show/739) - -[![L'histoire de l'IA par Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "L'histoire de l'IA par Amy Boyd") - -## Devoir - -[Créer une frise chronologique](assignment.fr.md) diff --git a/1-Introduction/2-history-of-ML/translations/README.id.md b/1-Introduction/2-history-of-ML/translations/README.id.md deleted file mode 100644 index 351dd17d..00000000 --- a/1-Introduction/2-history-of-ML/translations/README.id.md +++ /dev/null @@ -1,116 +0,0 @@ -# Sejarah Machine Learning - -![Ringkasan dari Sejarah Machine Learning dalam sebuah catatan sketsa](../../../sketchnotes/ml-history.png) -> Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac) - -## [Quiz Pra-Pelajaran](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/) - -Dalam pelajaran ini, kita akan membahas tonggak utama dalam sejarah Machine Learning dan Artificial Intelligence. - -Sejarah Artifical Intelligence, AI, sebagai bidang terkait dengan sejarah Machine Learning, karena algoritma dan kemajuan komputasi yang mendukung ML dimasukkan ke dalam pengembangan AI. Penting untuk diingat bahwa, meski bidang-bidang ini sebagai bidang-bidang penelitian yang berbeda mulai terbentuk pada 1950-an, [algoritmik, statistik, matematik, komputasi dan penemuan teknis](https://wikipedia.org/wiki/Timeline_of_machine_learning) penting sudah ada sebelumnya, dan saling tumpang tindih di era ini. Faktanya, orang-orang telah memikirkan pertanyaan-pertanyaan ini selama [ratusan tahun](https://wikipedia.org/wiki/History_of_artificial_intelligence): artikel ini membahas dasar-dasar intelektual historis dari gagasan 'mesin yang berpikir'. - -## Penemuan penting - -- 1763, 1812 [Bayes Theorem](https://wikipedia.org/wiki/Bayes%27_theorem) dan para pendahulu. Teorema ini dan penerapannya mendasari inferensi, mendeskripsikan kemungkinan suatu peristiwa terjadi berdasarkan pengetahuan sebelumnya. -- 1805 [Least Square Theory](https://wikipedia.org/wiki/Least_squares) oleh matematikawan Perancis Adrien-Marie Legendre. Teori ini yang akan kamu pelajari di unit Regresi, ini membantu dalam *data fitting*. -- 1913 [Markov Chains](https://wikipedia.org/wiki/Markov_chain) dinamai dengan nama matematikawan Rusia, Andrey Markov, digunakan untuk mendeskripsikan sebuah urutan dari kejadian-kejadian yang mungkin terjadi berdasarkan kondisi sebelumnya. -- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) adalah sebuah tipe dari *linear classifier* yang ditemukan oleh psikolog Amerika, Frank Rosenblatt, yang mendasari kemajuan dalam *Deep Learning*. -- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) adalah sebuah algoritma yang pada awalnya didesain untuk memetakan rute. Dalam konteks ML, ini digunakan untuk mendeteksi berbagai pola. -- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) digunakan untuk melatih [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network). -- 1982 [Recurrent Neural Networks](https://wikipedia.org/wiki/Recurrent_neural_network) adalah *artificial neural networks* yang berasal dari *feedforward neural networks* yang membuat grafik sementara. - -✅ Lakukan sebuah riset kecil. Tanggal berapa lagi yang merupakan tanggal penting dalam sejarah ML dan AI? -## 1950: Mesin yang berpikir - -Alan Turing, merupakan orang luar biasa yang terpilih oleh [publik di tahun 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) sebagai ilmuwan terhebat di abad 20, diberikan penghargaan karena membantu membuat fondasi dari sebuah konsep 'mesin yang bisa berpikir', Dia berjuang menghadapi orang-orang yang menentangnya dan keperluannya sendiri untuk bukti empiris dari konsep ini dengan membuat [Turing Test](https://www.bbc.com/news/technology-18475646), yang mana akan kamu jelajahi di pelajaran NLP kami. - -## 1956: Proyek Riset Musim Panas Dartmouth - -"Proyek Riset Musim Panas Dartmouth pada *artificial intelligence* merupakan sebuah acara penemuan untuk *artificial intelligence* sebagai sebuah bidang," dan dari sinilah istilah '*artificial intelligence*' diciptakan ([sumber](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)). - -> Setiap aspek pembelajaran atau fitur kecerdasan lainnya pada prinsipnya dapat dideskripsikan dengan sangat tepat sehingga sebuah mesin dapat dibuat untuk mensimulasikannya. - -Ketua peneliti, profesor matematika John McCarthy, berharap "untuk meneruskan dasar dari dugaan bahwa setiap aspek pembelajaran atau fitur kecerdasan lainnya pada prinsipnya dapat dideskripsikan dengan sangat tepat sehingga mesin dapat dibuat untuk mensimulasikannya." Marvin Minsky, seorang tokoh terkenal di bidang ini juga termasuk sebagai peserta penelitian. - -Workshop ini dipuji karena telah memprakarsai dan mendorong beberapa diskusi termasuk "munculnya metode simbolik, sistem yang berfokus pada domain terbatas (sistem pakar awal), dan sistem deduktif versus sistem induktif." ([sumber](https://wikipedia.org/wiki/Dartmouth_workshop)). - -## 1956 - 1974: "Tahun-tahun Emas" - -Dari tahun 1950-an hingga pertengahan 70-an, optimisme memuncak dengan harapan bahwa AI dapat memecahkan banyak masalah. Pada tahun 1967, Marvin Minsky dengan yakin menyatakan bahwa "Dalam satu generasi ... masalah menciptakan '*artificial intelligence*' akan terpecahkan secara substansial." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall) - -Penelitian *natural language processing* berkembang, pencarian disempurnakan dan dibuat lebih *powerful*, dan konsep '*micro-worlds*' diciptakan, di mana tugas-tugas sederhana diselesaikan menggunakan instruksi bahasa sederhana. - -Penelitian didanai dengan baik oleh lembaga pemerintah, banyak kemajuan dibuat dalam komputasi dan algoritma, dan prototipe mesin cerdas dibangun. Beberapa mesin tersebut antara lain: - -* [Shakey the robot](https://wikipedia.org/wiki/Shakey_the_robot), yang bisa bermanuver dan memutuskan bagaimana melakukan tugas-tugas secara 'cerdas'. - - ![Shakey, an intelligent robot](../images/shakey.jpg) - > Shakey pada 1972 - -* Eliza, sebuah 'chatterbot' awal, dapat mengobrol dengan orang-orang dan bertindak sebagai 'terapis' primitif. Kamu akan belajar lebih banyak tentang Eliza dalam pelajaran NLP. - - ![Eliza, a bot](../images/eliza.png) - > Sebuah versi dari Eliza, sebuah *chatbot* - -* "Blocks world" adalah contoh sebuah *micro-world* dimana balok dapat ditumpuk dan diurutkan, dan pengujian eksperimen mesin pengajaran untuk membuat keputusan dapat dilakukan. Kemajuan yang dibuat dengan *library-library* seperti [SHRDLU](https://wikipedia.org/wiki/SHRDLU) membantu mendorong kemajuan pemrosesan bahasa. - - [![blocks world dengan SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world dengan SHRDLU") - - > 🎥 Klik gambar diatas untuk menonton video: Blocks world with SHRDLU - -## 1974 - 1980: "Musim Dingin AI" - -Pada pertengahan 1970-an, semakin jelas bahwa kompleksitas pembuatan 'mesin cerdas' telah diremehkan dan janjinya, mengingat kekuatan komputasi yang tersedia, telah dilebih-lebihkan. Pendanaan telah habis dan kepercayaan dalam bidang ini menurun. Beberapa masalah yang memengaruhi kepercayaan diri termasuk: - -- **Keterbatasan**. Kekuatan komputasi terlalu terbatas. -- **Ledakan kombinatorial**. Jumlah parameter yang perlu dilatih bertambah secara eksponensial karena lebih banyak hal yang diminta dari komputer, tanpa evolusi paralel dari kekuatan dan kemampuan komputasi. -- **Kekurangan data**. Adanya kekurangan data yang menghalangi proses pengujian, pengembangan, dan penyempurnaan algoritma. -- **Apakah kita menanyakan pertanyaan yang tepat?**. Pertanyaan-pertanyaan yang diajukan pun mulai dipertanyakan kembali. Para peneliti mulai melontarkan kritik tentang pendekatan mereka - - Tes Turing mulai dipertanyakan, di antara ide-ide lain, dari 'teori ruang Cina' yang mengemukakan bahwa, "memprogram komputer digital mungkin membuatnya tampak memahami bahasa tetapi tidak dapat menghasilkan pemahaman yang sebenarnya." ([sumber](https://plato.stanford.edu/entries/chinese-room/)) - - Tantangan etika ketika memperkenalkan kecerdasan buatan seperti si "terapis" ELIZA ke dalam masyarakat. - -Pada saat yang sama, berbagai aliran pemikiran AI mulai terbentuk. Sebuah dikotomi didirikan antara praktik ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). Lab _Scruffy_ mengubah program selama berjam-jam sampai mendapat hasil yang diinginkan. Lab _Neat_ "berfokus pada logika dan penyelesaian masalah formal". ELIZA dan SHRDLU adalah sistem _scruffy_ yang terkenal. Pada tahun 1980-an, karena perkembangan permintaan untuk membuat sistem ML yang dapat direproduksi, pendekatan _neat_ secara bertahap menjadi yang terdepan karena hasilnya lebih dapat dijelaskan. - -## 1980s Sistem Pakar - -Seiring berkembangnya bidang ini, manfaatnya bagi bisnis menjadi lebih jelas, dan begitu pula dengan menjamurnya 'sistem pakar' pada tahun 1980-an. "Sistem pakar adalah salah satu bentuk perangkat lunak artificial intelligence (AI) pertama yang benar-benar sukses." ([sumber](https://wikipedia.org/wiki/Expert_system)). - -Tipe sistem ini sebenarnya adalah _hybrid_, sebagian terdiri dari mesin aturan yang mendefinisikan kebutuhan bisnis, dan mesin inferensi yang memanfaatkan sistem aturan untuk menyimpulkan fakta baru. - -Pada era ini juga terlihat adanya peningkatan perhatian pada jaringan saraf. - -## 1987 - 1993: AI 'Chill' - -Perkembangan perangkat keras sistem pakar terspesialisasi memiliki efek yang tidak menguntungkan karena menjadi terlalu terspesialiasasi. Munculnya komputer pribadi juga bersaing dengan sistem yang besar, terspesialisasi, dan terpusat ini. Demokratisasi komputasi telah dimulai, dan pada akhirnya membuka jalan untuk ledakan modern dari *big data*. - -## 1993 - 2011 - -Pada zaman ini memperlihatkan era baru bagi ML dan AI untuk dapat menyelesaikan beberapa masalah yang sebelumnya disebabkan oleh kurangnya data dan daya komputasi. Jumlah data mulai meningkat dengan cepat dan tersedia secara luas, terlepas dari baik dan buruknya, terutama dengan munculnya *smartphone* sekitar tahun 2007. Daya komputasi berkembang secara eksponensial, dan algoritma juga berkembang saat itu. Bidang ini mulai mengalami kedewasaan karena hari-hari yang tidak beraturan di masa lalu mulai terbentuk menjadi disiplin yang sebenarnya. - -## Sekarang - -Saat ini, *machine learning* dan AI hampir ada di setiap bagian dari kehidupan kita. Era ini menuntut pemahaman yang cermat tentang risiko dan efek potensi dari berbagai algoritma yang ada pada kehidupan manusia. Seperti yang telah dinyatakan oleh Brad Smith dari Microsoft, "Teknologi informasi mengangkat isu-isu yang menjadi inti dari perlindungan hak asasi manusia yang mendasar seperti privasi dan kebebasan berekspresi. Masalah-masalah ini meningkatkan tanggung jawab bagi perusahaan teknologi yang menciptakan produk-produk ini. Dalam pandangan kami, mereka juga menyerukan peraturan pemerintah yang bijaksana dan untuk pengembangan norma-norma seputar penggunaan yang wajar" ([sumber](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)). - -Kita masih belum tahu apa yang akan terjadi di masa depan, tetapi penting untuk memahami sistem komputer dan perangkat lunak serta algoritma yang dijalankannya. Kami berharap kurikulum ini akan membantu kamu untuk mendapatkan pemahaman yang lebih baik sehingga kamu dapat memutuskan sendiri. - -[![Sejarah Deep Learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "Sejarah Deep Learning") -> 🎥 Klik gambar diatas untuk menonton video: Yann LeCun mendiskusikan sejarah dari Deep Learning dalam pelajaran ini - ---- -## 🚀Tantangan - -Gali salah satu momen bersejarah ini dan pelajari lebih lanjut tentang orang-orang di baliknya. Ada karakter yang menarik, dan tidak ada penemuan ilmiah yang pernah dibuat dalam kekosongan budaya. Apa yang kamu temukan? - -## [Quiz Pasca-Pelajaran](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/) - -## Ulasan & Belajar Mandiri - -Berikut adalah item untuk ditonton dan didengarkan: - -[Podcast dimana Amy Boyd mendiskusikan evolusi dari AI](http://runasradio.com/Shows/Show/739) - -[![Sejarah AI oleh Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "Sejarah AI oleh Amy Boyd") - -## Tugas - -[Membuat sebuah *timeline*](assignment.id.md) diff --git a/1-Introduction/2-history-of-ML/translations/README.it.md b/1-Introduction/2-history-of-ML/translations/README.it.md deleted file mode 100644 index f95b542c..00000000 --- a/1-Introduction/2-history-of-ML/translations/README.it.md +++ /dev/null @@ -1,118 +0,0 @@ -# Storia di machine learning - -![Riepilogo della storia di machine learning in uno sketchnote](../../../sketchnotes/ml-history.png) -> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac) - -## [Quiz pre-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/?loc=it) - -In questa lezione, si camminerà attraverso le principali pietre miliari nella storia di machine learning e dell'intelligenza artificiale. - -La storia dell'intelligenza artificiale, AI, come campo è intrecciata con la storia di machine learning, poiché gli algoritmi e i progressi computazionali alla base di machine learning hanno contribuito allo sviluppo dell'intelligenza artificiale. È utile ricordare che, mentre questi campi come distinte aree di indagine hanno cominciato a cristallizzarsi negli anni '50, importanti [scoperte algoritmiche, statistiche, matematiche, computazionali e tecniche](https://wikipedia.org/wiki/Timeline_of_machine_learning) hanno preceduto e si sono sovrapposte a questa era. In effetti, le persone hanno riflettuto su queste domande per [centinaia di anni](https://wikipedia.org/wiki/History_of_artificial_intelligence); questo articolo discute le basi intellettuali storiche dell'idea di una "macchina pensante". - -## Scoperte rilevanti - -- 1763, 1812 [Teorema di Bayes](https://it.wikipedia.org/wiki/Teorema_di_Bayes) e suoi predecessori. Questo teorema e le sue applicazioni sono alla base dell'inferenza, descrivendo la probabilità che un evento si verifichi in base alla conoscenza precedente. -- 1805 [Metodo dei Minimi Quadrati](https://it.wikipedia.org/wiki/Metodo_dei_minimi_quadrati) del matematico francese Adrien-Marie Legendre. Questa teoria, che verrà trattata nell'unità Regressione, aiuta nell'adattamento dei dati. -- 1913 [Processo Markoviano](https://it.wikipedia.org/wiki/Processo_markoviano) dal nome del matematico russo Andrey Markov è usato per descrivere una sequenza di possibili eventi basati su uno stato precedente. -- 1957 [Percettrone](https://it.wikipedia.org/wiki/Percettrone) è un tipo di classificatore lineare inventato dallo psicologo americano Frank Rosenblatt che sta alla base dei progressi nel deep learning. -- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) è un algoritmo originariamente progettato per mappare i percorsi. In un contesto ML viene utilizzato per rilevare i modelli. -- 1970 [La Retropropagazione dell'Errore](https://it.wikipedia.org/wiki/Retropropagazione_dell'errore) viene utilizzata per addestrare [le reti neurali feed-forward](https://it.wikipedia.org/wiki/Rete_neurale_feed-forward). -- Le [Reti Neurali Ricorrenti](https://it.wikipedia.org/wiki/Rete_neurale_ricorrente) del 1982 sono reti neurali artificiali derivate da reti neurali feedforward che creano grafici temporali. - -✅ Fare una piccola ricerca. Quali altre date si distinguono come fondamentali nella storia del machine learning e dell'intelligenza artificiale? -## 1950: Macchine che pensano - -Alan Turing, una persona davvero notevole che è stata votata [dal pubblico nel 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) come il più grande scienziato del XX secolo, è accreditato per aver contribuito a gettare le basi per il concetto di "macchina in grado di pensare". Ha affrontato gli oppositori e il suo stesso bisogno di prove empiriche di questo concetto in parte creando il [Test di Turing](https://www.bbc.com/news/technology-18475646), che verrà esplorato nelle lezioni di NLP (elaborazione del linguaggio naturale). - -## 1956: Progetto di Ricerca Estivo Dartmouth - -"Il Dartmouth Summer Research Project sull'intelligenza artificiale è stato un evento seminale per l'intelligenza artificiale come campo", qui è stato coniato il termine "intelligenza artificiale" ([fonte](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)). - -> In linea di principio, ogni aspetto dell'apprendimento o qualsiasi altra caratteristica dell'intelligenza può essere descritto in modo così preciso che si può costruire una macchina per simularlo. - -Il ricercatore capo, il professore di matematica John McCarthy, sperava "di procedere sulla base della congettura che ogni aspetto dell'apprendimento o qualsiasi altra caratteristica dell'intelligenza possa in linea di principio essere descritta in modo così preciso che si possa costruire una macchina per simularlo". I partecipanti includevano un altro luminare nel campo, Marvin Minsky. - -Il workshop è accreditato di aver avviato e incoraggiato diverse discussioni tra cui "l'ascesa di metodi simbolici, sistemi focalizzati su domini limitati (primi sistemi esperti) e sistemi deduttivi contro sistemi induttivi". ([fonte](https://wikipedia.org/wiki/Dartmouth_workshop)). - -## 1956 - 1974: "Gli anni d'oro" - -Dagli anni '50 fino alla metà degli anni '70, l'ottimismo era alto nella speranza che l'AI potesse risolvere molti problemi. Nel 1967, Marvin Minsky dichiarò con sicurezza che "Entro una generazione... il problema della creazione di 'intelligenza artificiale' sarà sostanzialmente risolto". (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall) - -La ricerca sull'elaborazione del linguaggio naturale è fiorita, la ricerca è stata perfezionata e resa più potente ed è stato creato il concetto di "micro-mondi", in cui compiti semplici sono stati completati utilizzando istruzioni in linguaggio semplice. - -La ricerca è stata ben finanziata dalle agenzie governative, sono stati fatti progressi nel calcolo e negli algoritmi e sono stati costruiti prototipi di macchine intelligenti. Alcune di queste macchine includono: - -* [Shakey il robot](https://wikipedia.org/wiki/Shakey_the_robot), che poteva manovrare e decidere come eseguire i compiti "intelligentemente". - - ![Shakey, un robot intelligente](../images/shakey.jpg) - > Shakey nel 1972 - -* Eliza, una delle prime "chatterbot", poteva conversare con le persone e agire come una "terapeuta" primitiva. Si Imparerà di più su Eliza nelle lezioni di NLP. - - ![Eliza, un bot](../images/eliza.png) - > Una versione di Eliza, un chatbot - -* Il "mondo dei blocchi" era un esempio di un micromondo in cui i blocchi potevano essere impilati e ordinati e si potevano testare esperimenti su macchine per insegnare a prendere decisioni. I progressi realizzati con librerie come [SHRDLU](https://it.wikipedia.org/wiki/SHRDLU) hanno contribuito a far progredire l'elaborazione del linguaggio. - - [![Il mondo dei blocchi con SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "Il mondo dei blocchi con SHRDLU") - - > 🎥 Fare clic sull'immagine sopra per un video: Blocks world con SHRDLU - -## 1974 - 1980: "L'inverno dell'AI" - -Verso la metà degli anni '70, era diventato evidente che la complessità della creazione di "macchine intelligenti" era stata sottovalutata e che la sua promessa, data la potenza di calcolo disponibile, era stata esagerata. I finanziamenti si sono prosciugati e la fiducia nel settore è rallentata. Alcuni problemi che hanno influito sulla fiducia includono: - -- **Limitazioni**. La potenza di calcolo era troppo limitata. -- **Esplosione combinatoria**. La quantità di parametri necessari per essere addestrati è cresciuta in modo esponenziale man mano che veniva chiesto di più ai computer, senza un'evoluzione parallela della potenza e delle capacità di calcolo. -- **Scarsità di dati**. C'era una scarsità di dati che ostacolava il processo di test, sviluppo e perfezionamento degli algoritmi. -- **Stiamo facendo le domande giuste?**. Le stesse domande che venivano poste cominciarono ad essere messe in discussione. I ricercatori hanno iniziato a criticare i loro approcci: - - I test di Turing furono messi in discussione attraverso, tra le altre idee, la "teoria della stanza cinese" che postulava che "la programmazione di un computer digitale può far sembrare che capisca il linguaggio ma non potrebbe produrre una vera comprensione". ([fonte](https://plato.stanford.edu/entries/chinese-room/)) - - L'etica dell'introduzione di intelligenze artificiali come la "terapeuta" ELIZA nella società è stata messa in discussione. - -Allo stesso tempo, iniziarono a formarsi varie scuole di pensiero sull'AI. È stata stabilita una dicotomia tra pratiche ["scruffy" contro "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). I laboratori _scruffy_ ottimizzavano i programmi per ore fino a quando non ottenevano i risultati desiderati. I laboratori _Neat_ "si focalizzavano sulla logica e sulla risoluzione formale dei problemi". ELIZA e SHRDLU erano ben noti _sistemi scruffy_. Negli anni '80, quando è emersa la richiesta di rendere riproducibili i sistemi ML, l'_approccio neat_ ha gradualmente preso il sopravvento in quanto i suoi risultati sono più spiegabili. - -## Sistemi esperti degli anni '80 - -Man mano che il settore cresceva, i suoi vantaggi per le imprese diventavano più chiari e negli anni '80 lo stesso accadeva con la proliferazione di "sistemi esperti". "I sistemi esperti sono stati tra le prime forme di software di intelligenza artificiale (AI) di vero successo". ([fonte](https://wikipedia.org/wiki/Expert_system)). - -Questo tipo di sistema è in realtà _ibrido_, costituito in parte da un motore di regole che definisce i requisiti aziendali e un motore di inferenza che sfrutta il sistema di regole per dedurre nuovi fatti. - -Questa era ha visto anche una crescente attenzione rivolta alle reti neurali. - -## 1987 - 1993: AI 'Chill' - -La proliferazione di hardware specializzato per sistemi esperti ha avuto lo sfortunato effetto di diventare troppo specializzato. L'ascesa dei personal computer ha anche gareggiato con questi grandi sistemi centralizzati specializzati. La democratizzazione dell'informatica era iniziata e alla fine ha spianato la strada alla moderna esplosione dei big data. - -## 1993 - 2011 - -Questa epoca ha visto una nuova era per ML e AI per essere in grado di risolvere alcuni dei problemi che erano stati causati in precedenza dalla mancanza di dati e potenza di calcolo. La quantità di dati ha iniziato ad aumentare rapidamente e a diventare più ampiamente disponibile, nel bene e nel male, soprattutto con l'avvento degli smartphone intorno al 2007. La potenza di calcolo si è ampliata in modo esponenziale e gli algoritmi si sono evoluti di pari passo. Il campo ha iniziato a maturare quando i giorni a ruota libera del passato hanno iniziato a cristallizzarsi in una vera disciplina. - -## Adesso - -Oggi, machine learning e intelligenza artificiale toccano quasi ogni parte della nostra vita. Questa era richiede un'attenta comprensione dei rischi e dei potenziali effetti di questi algoritmi sulle vite umane. Come ha affermato Brad Smith di Microsoft, "La tecnologia dell'informazione solleva questioni che vanno al cuore delle protezioni fondamentali dei diritti umani come la privacy e la libertà di espressione. Questi problemi aumentano la responsabilità delle aziende tecnologiche che creano questi prodotti. A nostro avviso, richiedono anche un'attenta regolamentazione del governo e lo sviluppo di norme sugli usi accettabili" ([fonte](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)). - -Resta da vedere cosa riserva il futuro, ma è importante capire questi sistemi informatici e il software e gli algoritmi che eseguono. Ci si augura che questo programma di studi aiuti ad acquisire una migliore comprensione in modo che si possa decidere in autonomia. - -[![La storia del deeplearningLa](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 " storia del deep learning") -> 🎥 Fare clic sull'immagine sopra per un video: Yann LeCun discute la storia del deep learning in questa lezione - ---- - -## 🚀 Sfida - -Approfondire uno di questi momenti storici e scoprire - di più sulle persone che stanno dietro ad essi. Ci sono personaggi affascinanti e nessuna scoperta scientifica è mai stata creata in un vuoto culturale. Cosa si è scoperto? - -## [Quiz post-lezione](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/?loc=it) - -## Revisione e Auto Apprendimento - -Ecco gli elementi da guardare e ascoltare: - -[Questo podcast in cui Amy Boyd discute l'evoluzione dell'AI](http://runasradio.com/Shows/Show/739) - -[![La storia dell'AI di Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "La storia dell'AI di Amy Boyd") - -## Compito - -[Creare una sequenza temporale](assignment.it.md) diff --git a/1-Introduction/2-history-of-ML/translations/README.ja.md b/1-Introduction/2-history-of-ML/translations/README.ja.md deleted file mode 100644 index 780b1f85..00000000 --- a/1-Introduction/2-history-of-ML/translations/README.ja.md +++ /dev/null @@ -1,114 +0,0 @@ -# 機械学習の歴史 - -![機械学習の歴史をまとめたスケッチ](../../../sketchnotes/ml-history.png) -> [Tomomi Imura](https://www.twitter.com/girlie_mac)によるスケッチ - -## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3?loc=ja) - -この授業では、機械学習と人工知能の歴史における主要な出来事を紹介します。 - -人工知能（AI）の歴史は、機械学習の歴史と密接に関係しています。なぜならば、機械学習を支えるアルゴリズムと計算の進歩が、AIの発展につながったからです。これらの分野は、1950年代に明確になり始めましたが、重要な[アルゴリズム、統計、数学、計算、技術的な発見](https://wikipedia.org/wiki/Timeline_of_machine_learning)は、この時代よりも前に、そして同時に行われていたことを覚えておくとよいでしょう。実際、人々は[何百年も](https://wikipedia.org/wiki/History_of_artificial_intelligence)この問題について考えてきました。（この記事では、「考える機械」というアイデアの歴史的な知的基盤について説明されています。） - - -## 注目すべき発見 -- 1763年、1812年 [ベイズの定理](https://wikipedia.org/wiki/Bayes%27_theorem)とその前身の発見。ある事象が起こる確率を、事前の知識に基づいて記述する推論の基礎となる定理とその応用。 -- 1805年 フランスの数学者アドリアン＝マリー・レジェンドルによる[最小二乗理論](https://wikipedia.org/wiki/Least_squares)。この理論は、データのフィッティングに役立つ。 -- 1913年 ロシアの数学者アンドレイ・マルコフにちなんで名付けられた[マルコフ連鎖](https://wikipedia.org/wiki/Markov_chain)は、以前の状態に基づいて起こりうる一連の事象を記述するために使用される。 -- 1957年 [パーセプトロン](https://wikipedia.org/wiki/Perceptron)は、アメリカの心理学者フランク・ローゼンブラットが発明した線形分類器の一種であり、深層学習の基盤となっている。 -- 1967 [最小近傍法](https://wikipedia.org/wiki/Nearest_neighbor)は、元々は経路探索のために考案されたアルゴリズム。MLではパターンの検出に用いられる。 -- 1970年 [バックプロパゲーション](https://wikipedia.org/wiki/Backpropagation)を用いて[フィードフォワード・ニューラルネットワーク（順伝播型ニューラルネットワーク）](https://wikipedia.org/wiki/Feedforward_neural_network)を学習する。 -- 1982年 [回帰型ニューラルネットワーク](https://wikipedia.org/wiki/Recurrent_neural_network) は、フィードフォワード・ニューラルネットワークから派生した人工的なニューラルネットワークで、時間的なグラフを作成します。 - -✅ 少し調べてみてください。MLとAIの歴史の中で重要な日付は他にありますか？ - -## 1950: 思考する機械 -アラン・チューリングは、[2019年に世間から](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century)20世紀最大の科学者として投票された、真に優れた人物で、「考えることができる機械」という概念の基礎を築くのに貢献したとされています。彼は、否定的な意見や、この概念の実証的な証拠を必要とする自分自身と、この先自然言語処理の授業で触れることとなる[チューリング・テスト](https://www.bbc.com/news/technology-18475646)を作成することで戦いました。 - -## 1956: ダートマス・サマー・リサーチ・プロジェクト -ダートマス・サマー・リサーチ・プロジェクトは、分野としての人工知能にとって重要な出来事であり、ここで「人工知能」という言葉が作られました（[出典](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)） - -> 学習やその他の知能のあらゆる側面は、原理的に非常に正確に記述することができるので、それをシミュレートする機械を作ることができる。 - -主任研究者である数学のジョン・マッカーシー教授は、「学習のあらゆる側面や知能のその他の特徴は、原理的に非常に正確に記述することができるので、それをシミュレートする機械を作ることができるという推測に基づいて進めていきたい」と考えていました。参加者の中には、この分野の著名人であるマービン・ミンスキーもいました。 - -このワークショップでは、「記号的手法の台頭、限定された領域に焦点を当てたシステム（初期のエキスパートシステム）、演繹的システムと帰納的システムの比較」などの議論が開始され、促進されたと評価されています。（[出典](https://wikipedia.org/wiki/Dartmouth_workshop)） - -## 1956 - 1974: 黄金期 - -1950年代から70年代半ばまでは、AIがさまざまな問題を解決してくれるのではないかという楽観的な見方が広がっていました。1967年、マービン・ミンスキーは「一世代のうちに...『人工知能』を作るという問題は実質的に解決されるだろう」と自信を持って述べている。(Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall) - -自然言語処理の研究が盛んになり、検索が洗練されてより強力になり、平易な言語による指示で簡単な作業をこなす「マイクロワールド」という概念が生まれた。 - -研究は政府機関から潤沢な資金が提供され、計算とアルゴリズムが進歩し、知的機械のプロトタイプが作られた。その中には次のようなものがある。 - -* 移動したり、タスクを実行する方法を「知的に」決定することができるロボット[「Shakey」](https://wikipedia.org/wiki/Shakey_the_robot) - - ![知的なロボットであるShakey](../images/shakey.jpg) - > 1972のShakey - -* 初期の「おしゃべりロボット」であるElizaは、人と会話することができ、原始的な「セラピスト」の役割を果たした。エリザについては、NLPのレッスンで詳しく説明します。 - - ![BotであるEliza](../images/eliza.png) - > チャットボットEliza - -* 「Blocks world」は、ブロックを積み上げたり並べ替えたりするマイクロワールドの一例で、機械に判断力を身につけさせる実験を行った。[SHRDLU](https://wikipedia.org/wiki/SHRDLU)をはじめとするライブラリの進歩は、言語処理の発展に大きく貢献した。 - - [![SHRDLUを用いたblocks world](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "SHRDLUを用いたblocks world") - - > 🎥 上の画像をクリックすると動画が見られます："SHRDLUを用いたblocks world" - -## 1974 - 1980: AIの冬 - -1970年代半ばになると、「知的な機械」を作ることの複雑さが過小評価されていたことや、利用可能な計算能力を考慮すると、その将来性が過大評価されていたことが明らかになりました。資金が枯渇し、この分野への信頼が低下した。信頼性に影響を与えた問題には以下のようなものがある。: - -- **限界**. 計算能力の限界 -- **組み合わせの爆発**. 学習に必要なパラメータの量は、コンピュータに要求されることが多くなるにつれて指数関数的に増加しましたが、コンピュータの性能や能力は並行して進化しませんでした。 -- **データの少なさ**. データが不足していたため、アルゴリズムのテスト、開発、改良のプロセスが妨げられた。 -- **正しい質問をしているのかどうか**. 問いかけていた質問そのものが疑問視され始めた。研究者たちは、自分たちのアプローチに批判的な意見を持つようになった。 - - チューリングテストは、「コンピュータをプログラミングすることで、言語を理解しているように見せかけることはできるが、本当の意味での理解はできない」とする「チャイニーズルーム理論」などによって、疑問視されるようになった。([出典](https://plato.stanford.edu/entries/chinese-room/)) - - セラピストとしてELIZAのような人工知能を社会に導入することの倫理性が問われた。 -それと同時に、さまざまなAIの流派が形成され始めました。一つは、["Scruffy"と "Neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies)という二分法である。Scruffyな研究室では、目的の結果が得られるまで何時間もプログラムをいじっていた一方、neatな研究室では、論理と形式的な問題解決を重視する。ELIZAやSHRDLUなどが有名なScruffyであるシステムである。1980年代に入って、MLシステムの再現性が求められるようになると、結果が説明可能であることから、次第にneatなアプローチが主流になっていきました。 - -## 1980s エキスパートシステム - -分野が発展するにつれ、ビジネスへの貢献が明確になり、1980年代には「エキスパートシステム」が普及しました。「エキスパートシステムは、人工知能（AI）ソフトウェアの中で最初に真に成功した形態の一つである。」と言われています。（[出典](https://wikipedia.org/wiki/Expert_system)） - -このタイプのシステムは、ビジネス要件を定義するルールエンジンと、ルールシステムを活用して新たな事実を推論する推論エンジンで構成されるハイブリッド型です。 - -また、この時代はニューラルネットワークにも注目が集まった。 - -## 1987 - 1993: AIの冷え込み - -専門分野に特化したエキスパートシステムのハードウェアが普及したことで、専門性が高くなりすぎてしまうという残念な結果になりました。また、パーソナルコンピュータの台頭は、これらの大規模で専門的な中央集権的システムと競合した。コンピューティングの民主化が始まり、最終的には現代の爆発的なビッグデータへの道が開かれました。 - -## 1993 - 2011 - -この期間では、それ以前にデータと計算能力の不足によって引き起こされていた問題を、MLやAIが解決できるようになっていた。特に2007年頃にスマートフォンが登場したことで、良くも悪くもデータ量が急速に増加し、広く利用されるようになりました。計算機の性能も飛躍的に向上し、アルゴリズムもそれに合わせて進化していきました。過去の自由奔放な時代から、真の学問としての結晶化が始まり、この分野は成熟していきました。 - -## 現在 - -現在、機械学習やAIは、私たちの生活のほぼすべての部分に関わっています。このような時代には、これらのアルゴリズムが人間の生活に及ぼすリスクや潜在的な影響を注意深く理解することが求められます。マイクロソフトのブラッド・スミスは、「情報技術は、プライバシーや表現の自由といった基本的な人権保護の核心に迫る問題を提起します。情報技術は、プライバシーや表現の自由といった基本的な人権保護の根幹に関わる問題を提起します。我々の見解では、これらの問題は、政府による思慮深い規制と、許容される使用方法に関する規範の策定を必要としています。」と述べています。（[出典](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)） - -未来がどうなるかはまだわかりませんが、これらのコンピュータシステムと、それを動かすソフトウェアやアルゴリズムを理解することは重要です。このカリキュラムが自身で判断するにあたり、より良い理解を助けるものになると幸いです。 - -[![ディープラーニングの歴史](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "ディープラーニングの歴史") -> 🎥 上の画像をクリックすると動画が見られます：このレクチャーではYann LeCunがディープラーニングの歴史について議論しています。 - ---- -## 🚀Challenge - -これらの歴史的瞬間の1つを掘り下げて、その背後にいる人々について学びましょう。魅力的な人々がいますし、文化的に空白の状態で科学的発見がなされたことはありません。どういったことが見つかるでしょうか？ - -## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4?loc=ja) - -## 振り返りと自習 - -視聴するべき教材は以下になります: - -[Amy BoydがAIの進化について述べているポッドキャスト](http://runasradio.com/Shows/Show/739) - -[![Amy BoydによるAIの歴史](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "Amy BoydによるAIの歴史") - -## 課題 - -[年表を作成する](./assignment.ja.md) diff --git a/1-Introduction/2-history-of-ML/translations/README.ko.md b/1-Introduction/2-history-of-ML/translations/README.ko.md deleted file mode 100644 index cf0a1dae..00000000 --- a/1-Introduction/2-history-of-ML/translations/README.ko.md +++ /dev/null @@ -1,118 +0,0 @@ -# 머신러닝의 역사 - -![Summary of History of machine learning in a sketchnote](../../../sketchnotes/ml-history.png) -> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac) - -## [강의 전 퀴즈](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/) - -이 강의에서, 머신러닝과 인공 지능의 역사에서 주요 마일스톤을 살펴보려 합니다. - -인공 지능, AI의 역사는 머신러닝의 역사와 서로 엮여 있으며, ML을 받쳐주는 알고리즘과 계산 기술이 AI의 개발에 기여했습니다. 독특한 탐구 영역으로 이런 분야는 1950년에 구체적으로 시작했지만, 중요한 [algorithmical, statistical, mathematical, computational and technical discoveries](https://wikipedia.org/wiki/Timeline_of_machine_learning)로 이 시대를 오버랩했다고 생각하는 게 유용합니다. 실제로, 사람들은 [hundreds of years](https://wikipedia.org/wiki/History_of_artificial_intelligence)동안 이 질문을 생각해왔습니다: 이 아티클은 'thinking machine'라는 개념의 역사적 지적 토대에 대하여 이야기 합니다. - -## 주목할 발견 - -- 1763, 1812 [Bayes Theorem](https://wikipedia.org/wiki/Bayes%27_theorem)과 전임자. 이 정리와 적용은 사전지식 기반으로 이벤트가 발생할 확률을 설명할 추론의 기초가 됩니다. -- 1805 [Least Square Theory](https://wikipedia.org/wiki/Least_squares) by 프랑스 수학자 Adrien-Marie Legendre. Regression 단위에서 배울 이 이론은, 데이터 피팅에 도움이 됩니다. -- 1913 러시아 수학자 Andrey Markov의 이름에서 유래된 [Markov Chains](https://wikipedia.org/wiki/Markov_chain)는 이전 상태를 기반으로 가능한 이벤트의 시퀀스를 설명하는 데 사용됩니다. -- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron)은 미국 심리학자 Frank Rosenblatt이 개발한 linear classifier의 한 타입으로 딥러닝 발전을 뒷받칩니다. -- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor)는 원래 경로를 맵핑하기 위한 알고리즘입니다. ML context에서 패턴을 감지할 때 사용합니다. -- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation)은 [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network)를 학습할 때 사용합니다. -- 1982 [Recurrent Neural Networks](https://wikipedia.org/wiki/Recurrent_neural_network)는 시간 그래프를 생성하는 feedforward neural networks에서 파생한 인공 신경망입니다. - -✅ 조금 조사해보세요. ML과 AI의 역사에서 중요한 다른 날짜는 언제인가요? - -## 1950: 생각하는 기계 - -20세기의 최고 과학자로 [by the public in 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century)에 선택된, Alan Turing은, 'machine that can think.'라는 개념의 기반을 구축하는 데에 기여한 것으로 평가되고 있습니다. -NLP 강의에서 살필 [Turing Test](https://www.bbc.com/news/technology-18475646)를 만들어서 부분적으로 이 개념에 대한 경험적인 반대하는 사람들과 대립했습니다. - -## 1956: Dartmouth 여름 연구 프로젝트 - -"The Dartmouth Summer Research Project on artificial intelligence was a seminal event for artificial intelligence as a field," ([source](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth))에서 "인공 지능"이라는 용어가 만들어졌습니다. - -> 학습의 모든 측면이나 지능의 다른 기능은 원칙적으로 정확하게 서술할 수 있어서 이를 따라 할 기계를 만들 수 있습니다. - -수석 연구원인, 수학 교수 John McCarthy는, "to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."이라고 희망했습니다. 참가한 사람들 중에서는 Marvin Minsky도 있었습니다. - -이 워크숍은 "the rise of symbolic methods, systems focussed on limited domains (early expert systems), and deductive systems versus inductive systems." ([source](https://wikipedia.org/wiki/Dartmouth_workshop))을 포함해서 여러 토론을 시작하고 장려한 것으로 평가됩니다. - -## 1956 - 1974: "The golden years" - -1950년대부터 70년대 중순까지 AI로 많은 문제를 해결할 수 있다고 믿은 낙관주의가 커졌습니다. 1967년 Marvin Minsky는 "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)이라고 자신있게 말했습니다. - -natural language processing 연구가 발전하고, 검색이 개선되어 더 강력해졌으며, 단순한 언어 지침으로 간단한 작업을 완료하는 'micro-worlds'라는 개념이 생겼습니다. - -정부 지원을 받으며 연구했으며, 계산과 알고리즘이 발전하면서, 지능적 기계의 프로토 타입이 만들어졌습니다. 이런 기계 중에 일부는 아래와 같습니다: - -* [Shakey the robot](https://wikipedia.org/wiki/Shakey_the_robot), '지능적'으로 작업하는 방법을 조종하고 결정할 수 있습니다. - - ![Shakey, an intelligent robot](../images/shakey.jpg) - > Shakey in 1972 - -* 초기 'chatterbot'인, Eliza는, 사람들과 이야기하고 원시적 '치료사' 역할을 할 수 있었습니다. NLP 강의에서 Eliza에 대하여 자세히 알아봅시다. - - ![Eliza, a bot](../images/eliza.png) - > A version of Eliza, a chatbot - -* "Blocks world"는 블록을 쌓고 분류할 수 있는 마이크로-월드의 예시이며, 결정하는 기계를 가르칠 실험을 테스트할 수 있었습니다. [SHRDLU](https://wikipedia.org/wiki/SHRDLU)와 같은 라이브러리로 만들어진 발명은 language processing를 발전시키는 데 도움이 되었습니다. - - [![blocks world with SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world with SHRDLU") - - > 🎥 영상을 보려면 이미지 클릭: Blocks world with SHRDLU - -## 1974 - 1980: "AI Winter" - -1970년 중순에, '인공 기계'를 만드는 복잡도가 과소 평가되면서, 주어진 컴퓨터 파워를 고려해보니, 그 약속은 과장된 것이 분명해졌습니다. 자금이 고갈되고 현장에 대한 자신감도 느려졌습니다. 신뢰에 영향을 준 이슈는 아래에 있습니다: - -- **제한**. 컴퓨터 성능이 너무 제한되었습니다. -- **결합 파열**. 훈련에 필요한 파라미터의 양이 컴퓨터 성능, 기능과 별개로 컴퓨터의 요청에 따라 늘어났습니다. -- **데이터 부족**. 알고리즘을 테스트, 개발, 그리고 개선할 수 없게 데이터가 부족했습니다. -- **올바른 질문인가요?**. 질문받은 그 질문에 바로 물었습니다. 연구원들은 그 접근 방식에 비판했습니다: - - 튜링 테스트는 "programming a digital computer may make it appear to understand language but could not produce real understanding." ([source](https://plato.stanford.edu/entries/chinese-room/))하다고 가정한, 'chinese room theory'의 다른 아이디어에 의해 의문이 생겼습니다. - - "치료사" ELIZA와 같은 인공 지능을 사회에 도입하며 윤리에 도전했습니다. - -동 시간대에, 다양한 AI 학교가 형성되기 시작했습니다. ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies) 사이에 이분법이 확립되었습니다. _Scruffy_ 연구실은 원하는 결과를 얻을 때까지 몇 시간 동안 프로그램을 트윅했습니다. _Neat_ 연구실은 논리와 공식적 문제를 해결하는 데에 초점을 맞추었습니다. ELIZA와 SHRDLU는 잘 알려진 _scruffy_ 시스템입니다. 1980년대에, ML 시스템을 재현할 수 있어야 된다는 요구사항이 생겼고, _neat_ 방식이 더 결과를 설명할 수 있어서 점차 선두를 차지했습니다. - -## 1980s 전문가 시스템 - -이 분야가 성장하며, 비즈니스에 대한 이점이 명확해졌고, 1980년대에 '전문가 시스템'이 확산되었습니다. "Expert systems were among the first truly successful forms of artificial intelligence (AI) software." ([source](https://wikipedia.org/wiki/Expert_system)). - -이 시스템의 타입은, 실제로 비즈니스 요구사항을 정의하는 룰 엔진과 새로운 사실 추론하는 룰 시스템을 활용한 추론 엔진으로 부분적 구성된 _hybrid_ 입니다. - -이런 시대에도 neural networks에 대한 관심이 늘어났습니다. - -## 1987 - 1993: AI 'Chill' - -전문화된 전문가 시스템 하드웨어의 확산은 너무나도 고차원되는 불운한 결과를 가져왔습니다. 개인용 컴퓨터의 부상은 크고, 전문화된, 중앙화 시스템과 경쟁했습니다. 컴퓨팅의 민주화가 시작되었고, 결국 현대의 빅 데이터 폭발을 위한 길을 열었습니다. - -## 1993 - 2011 - -이 시대에는 ML과 AI가 과거 데이터와 컴퓨터 파워 부족으로 인해 발생했던 문제 중 일부 해결할 수 있는 새로운 시대가 열렸습니다. 데이터의 양은 급격히 늘어나기 시작했고, 2007년에 스마트폰이 나오면서 좋든 나쁘든 더 넓게 사용할 수 있게 되었습니다. 컴퓨터 파워는 크게 확장되었고, 알고리즘도 함께 발전했습니다. 과거 자유롭던 시대에서 진정한 규율로 이 분야는 성숙해지기 시작했습니다. - -## 현재 - -오늘 날, 머신러닝과 AI는 인생의 대부분에 영향을 미칩니다. 이 시대에는 이러한 알고리즘이 인간의 인생에 미치는 위험과 잠재적인 영향에 대한 주의깊은 이해도가 요구됩니다. Microsoft의 Brad Smith가 언급합니다 "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)). - -미래가 어떻게 변할지 알 수 없지만, 컴퓨터 시스템과 이를 실행하는 소프트웨어와 알고리즘을 이해하는 것은 중요합니다. 이 커리큘럼으로 더 잘 이해하고 스스로 결정할 수 있게 되기를 바랍니다. - -[![The history of deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning") -> 🎥 영상 보려면 위 이미지 클릭: Yann LeCun이 강의에서 딥러닝의 역사를 이야기 합니다. - ---- -## 🚀 도전 - -역사적인 순간에 사람들 뒤에서 한 가지를 집중적으로 파고 있는 자를 자세히 알아보세요. 매력있는 캐릭터가 있으며, 문화가 사라진 곳에서는 과학적인 발견을 하지 못합니다. 당신은 어떤 발견을 해보았나요? - -## [강의 후 퀴즈](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/) - -## 검토 & 자기주도 학습 - -보고 들을 수 있는 항목은 아래와 같습니다: - -[This podcast where Amy Boyd discusses the evolution of AI](http://runasradio.com/Shows/Show/739) - -[![The history of AI by Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd") - -## 과제 - -[Create a timeline](../assignment.md) diff --git a/1-Introduction/2-history-of-ML/translations/README.pt-br.md b/1-Introduction/2-history-of-ML/translations/README.pt-br.md deleted file mode 100644 index 815c8722..00000000 --- a/1-Introduction/2-history-of-ML/translations/README.pt-br.md +++ /dev/null @@ -1,118 +0,0 @@ -# História do machine learning - -![Resumo da história do machine learning no sketchnote](../../../sketchnotes/ml-history.png) -> Sketchnote por [Tomomi Imura](https://www.twitter.com/girlie_mac) - -## [Teste pré-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3?loc=ptbr) - -Nesta lição, veremos os principais marcos da história do machine learning e da artificial intelligence. - -A história da inteligência artificial, IA, como um campo está entrelaçada com a história do machine learning, pois os algoritmos e os avanços computacionais por trás do machine learning contribuíram para o desenvolvimento da inteligência artificial. É útil lembrar que, embora esses campos como áreas distintas de investigação tenham começado a se cristalizar na década de 1950, importantes [descobertas algorítmicas, estatísticas, matemáticas, computacionais e técnicas](https://wikipedia.org/wiki/Timeline_of_machine_learning) -precederam e se sobrepuseram com esta época. Na verdade, as pessoas têm refletido sobre essas questões por [centenas de anos](https://wikipedia.org/wiki/History_of_artificial_intelligence): este artigo discute a base intelectual histórica da ideia de uma 'máquina pensante'. - -## Descobertas notáveis - -- 1763, 1812 [Teorema de Bayes](https://wikipedia.org/wiki/Bayes%27_theorem) e seus predecessores. Este teorema e suas aplicações fundamentam a inferência, descrevendo a probabilidade de um evento ocorrer com base em conhecimento prévio. -- 1805 [Teoria dos Mínimos Quadrados](https://wikipedia.org/wiki/Least_squares) pelo matemático francês Adrien-Marie Legendre. Esta teoria, que você aprenderá em nossa unidade de regressão, ajuda no ajuste de dados. -- 1913 [Cadeias de Markov](https://wikipedia.org/wiki/Markov_chain) com o nome do matemático russo Andrey Markov é usado para descrever uma sequência de eventos possíveis com base em um estado anterior. -- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) é um tipo de classificador linear inventado pelo psicólogo americano Frank Rosenblatt que fundamenta os avanços no aprendizado profundo. -- 1967 [Vizinho mais próximo](https://wikipedia.org/wiki/Nearest_neighbor) é um algoritmo originalmente projetado para mapear rotas. Em um contexto de ML, ele é usado para detectar padrões. -- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) é usado para treinar [redes neurais feedforward](https://wikipedia.org/wiki/Feedforward_neural_network). -- 1982 [Redes Neurais Recorrentes](https://wikipedia.org/wiki/Recurrent_neural_network) são redes neurais artificiais derivadas de redes neurais feedforward que criam gráficos temporais. - -✅ Faça uma pequena pesquisa. Que outras datas se destacam como fundamentais na história do ML e da AI? - -## 1950: Máquinas que pensam - -Alan Turing, uma pessoa verdadeiramente notável que foi eleita [pelo público em 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) como o maior cientista do século 20, é creditado por ajudar a lançar as bases para o conceito de uma 'máquina que pode pensar'. Ele lutou contra os pessimistas e sua própria necessidade de evidências empíricas desse conceito, em parte criando o [Teste de Turing](https://www.bbc.com/news/technology-18475646), que você explorará em nossas lições de NPL. - -## 1956: Projeto de Pesquisa de Verão de Dartmouth - -"O Projeto de Pesquisa de Verão de Dartmouth sobre inteligência artificial foi um evento seminal para a inteligência artificial como um campo", e foi aqui que o termo 'inteligência artificial (AI)' foi cunhado ([fonte](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)). - -> Cada aspecto da aprendizagem ou qualquer outra característica da inteligência pode, em princípio, ser descrito com tanta precisão que uma máquina pode ser feita para simulá-lo. - -O pesquisador principal, professor de matemática John McCarthy, esperava "proceder com base na conjectura de que cada aspecto do aprendizado ou qualquer outra característica da inteligência pode, em princípio, ser descrito de forma tão precisa que uma máquina pode ser feita para simulá-lo". Os participantes incluíram outro luminar da área, Marvin Minsky. - -O workshop é creditado por ter iniciado e encorajado várias discussões, incluindo "o surgimento de métodos simbólicos, sistemas focados em domínios limitados (primeiros sistemas especialistas) e sistemas dedutivos versus sistemas indutivos." ([fonte](https://wikipedia.org/wiki/Dartmouth_workshop)). - -## 1956 - 1974: "Os anos dourados" - -Dos anos 1950 até meados dos anos 1970, o otimismo era alto na esperança de que a IA pudesse resolver muitos problemas. Em 1967, Marvin Minsky afirmou com segurança que "dentro de uma geração ... o problema de criar 'inteligência artificial' será substancialmente resolvido." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall) - -A pesquisa em processamento de linguagem natural floresceu, a pesquisa foi refinada e tornada mais poderosa, e o conceito de "micro-mundos" foi criado, no qual tarefas simples são concluídas usando instruções de linguagem simples. - -A pesquisa foi bem financiada por agências governamentais, avanços foram feitos em computação e algoritmos e protótipos de máquinas inteligentes foram construídos. Algumas dessas máquinas incluem: - -* [Shakey o robô](https://wikipedia.org/wiki/Shakey_the_robot), quem poderia manobrar e decidir como realizar as tarefas de forma 'inteligente'. - - ![Shakey, o robô inteligente](../images/shakey.jpg) - > Shakey em 1972 - -* Eliza, um dos primeiros 'chatterbot', podia conversar com as pessoas e agir como uma 'terapeuta' primitiva. Você aprenderá mais sobre Eliza nas lições de NPL. - - ![Eliza, a bot](../images/eliza.png) - > Uma versão de Eliza, um chatbot - -* O "mundo dos blocos" era um exemplo de micro-mundo onde os blocos podiam ser empilhados e classificados, e experimentos em máquinas de ensino para tomar decisões podiam ser testados. Avanços construídos com bibliotecas como [SHRDLU](https://wikipedia.org/wiki/SHRDLU) ajudaram a impulsionar o processamento de linguagem. - - [![mundo dos blocos com SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "mundo dos blocos com SHRDLU") - - > 🎥 Clique na imagem acima para ver um vídeo: Mundo dos blocos com SHRDLU - -## 1974 - 1980: "O inverno da AI" - -Em meados da década de 1970, ficou claro que a complexidade de criar 'máquinas inteligentes' havia sido subestimada e que sua promessa, dado o poder de computação disponível, havia sido exagerada. O financiamento secou e a confiança no setor desacelerou. Alguns problemas que afetaram a confiança incluem: - -- **Limitações**. O poder de computação era muito limitado. -- **Explosão combinatória**. A quantidade de parâmetros que precisavam ser treinados cresceu exponencialmente à medida que mais computadores eram exigidos, sem uma evolução paralela no poder e nas capacidades de computação. -- **Falta de dados**. Havia uma escassez de dados que dificultou o processo de teste, desenvolvimento e refinamento dos algoritmos. -- **Estamos fazendo as perguntas certas?**. As próprias perguntas que estavam sendo feitas começaram a ser questionadas. Os pesquisadores começaram a criticar suas abordagens: - - Os testes de Turing foram desafiados através, entre outras ideias, da 'teoria da sala chinesa' que postulava que "programar um computador digital pode fazer com que pareça compreender a linguagem, mas não pode produzir uma compreensão verdadeira". ([fonte](https://plato.stanford.edu/entries/chinese-room/)) - - A ética da introdução de inteligências artificiais como o "terapeuta" de ELIZA na sociedade tem sido questionada. - -Ao mesmo tempo, várias escolas de pensamento de IA começaram a se formar. Uma dicotomia foi estabelecida entre as práticas ["scruffy" versus "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). O laboratório do _Scruffy_ otimizou os programas por horas até obter os resultados desejados. O laboratório do _Neat_ "focava na solução de problemas lógicos e formais". ELIZA e SHRDLU eram sistemas desalinhados bem conhecidos. Na década de 1980, quando surgiu a demanda para tornar os sistemas de ML reproduzíveis, a abordagem _neat_ gradualmente assumiu o controle, à medida que seus resultados eram mais explicáveis. - -## Sistemas especialistas de 1980 - -À medida que o campo cresceu, seus benefícios para os negócios tornaram-se mais claros e, na década de 1980, o mesmo aconteceu com a proliferação de 'sistemas especialistas'. "Os sistemas especialistas estavam entre as primeiras formas verdadeiramente bem-sucedidas de software de inteligência artificial (AI)." ([fonte](https://wikipedia.org/wiki/Expert_system)). - -Na verdade, esse tipo de sistema é _híbrido_, consistindo parcialmente em um mecanismo de regras que define os requisitos de negócios e um mecanismo de inferência que potencializa o sistema de regras para deduzir novos fatos. - -Essa era também viu uma crescente atenção dada às redes neurais. - -## 1987 - 1993: AI 'Chill' - -A proliferação de hardware de sistemas especialistas especializados teve o infeliz efeito de se tornar muito especializado. A ascensão dos computadores pessoais também competiu com esses sistemas grandes, especializados e centralizados. A democratização da computação havia começado e, por fim, pavimentou o caminho para a explosão moderna de big data. - -## 1993 - 2011 - -Essa época viu uma nova era para o ML e a AI serem capazes de resolver alguns dos problemas que eram causados anteriormente pela falta de dados e capacidade de computação. A quantidade de dados começou a aumentar rapidamente e se tornar mais amplamente disponível, para melhor e para pior, especialmente com o advento do smartphone por volta de 2007. O poder de computação se expandiu exponencialmente e os algoritmos evoluíram junto. O campo começou a ganhar maturidade à medida que os dias livres do passado começaram a se cristalizar em uma verdadeira disciplina. - -## Agora - -Hoje, o machine learning e a inteligência artificial afetam quase todas as partes de nossa vida. Esta era requer uma compreensão cuidadosa dos riscos e efeitos potenciais desses algoritmos em vidas humanas. Como disse Brad Smith, da Microsoft, "a tecnologia da informação levanta questões que vão ao cerne das proteções fundamentais dos direitos humanos, como privacidade e liberdade de expressão. Essas questões aumentam a responsabilidade das empresas de tecnologia que criam esses produtos. Observe, elas também exigem um governo cuidadoso regulamentação e o desenvolvimento de padrões sobre usos aceitáveis​​" ([fonte](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)). - -Resta saber o que o futuro reserva, mas é importante entender esses sistemas de computador e o software e algoritmos que eles executam. Esperamos que este curso lhe ajude a obter um melhor entendimento para que você possa decidir por si mesmo. - -[![A história do deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "A história do deep learning") -> 🎥 Clique na imagem acima para ver um vídeo: Yann LeCun discute a história do deep learning nesta palestra - ---- -## 🚀Desafio - -Explore um desses momentos históricos e aprenda mais sobre as pessoas por trás deles. Existem personagens fascinantes e nenhuma descoberta científica foi criada em um vácuo cultural. O que você descobriu? - -## [Questionário pós-aula](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4?loc=ptbr) - -## Revisão e Autoestudo - -Aqui estão os itens para assistir e ouvir: - -[Este podcast em que Amy Boyd discute a evolução da AI](http://runasradio.com/Shows/Show/739) - -[![A história da AI ​​por Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "A história da AI ​​por Amy Boyd") - -## Tarefa - -[Crie uma linha do tempo](assignment.pt-br.md) diff --git a/1-Introduction/2-history-of-ML/translations/README.ru.md b/1-Introduction/2-history-of-ML/translations/README.ru.md deleted file mode 100644 index 013cf9cb..00000000 --- a/1-Introduction/2-history-of-ML/translations/README.ru.md +++ /dev/null @@ -1,146 +0,0 @@ -# История машинного обучения - -![Краткое изложение истории машинного обучения в заметке](../../../sketchnotes/ml-history.png) -> Заметка [Томоми Имура](https://www.twitter.com/girlie_mac) - -## [Тест перед лекцией](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/) - ---- - -На этом уроке мы рассмотрим основные вехи в истории машинного обучения и искусственного интеллекта. - -История искусственного интеллекта (ИИ) как области переплетается с историей машинного обучения (machine learning, ML), поскольку алгоритмы и вычислительные достижения, лежащие в основе ML, способствовали развитию ИИ. Полезно помнить, что, хотя эти области как начали выделяться в отдельные в 1950-х годах, важные [алгоритмические, статистические, математические, вычислительные и технические открытия](https://wikipedia.org/wiki/Timeline_of_machine_learning) предшествовали и происходили в эту эпоху. На самом деле, люди думали об этих вопросах в течение [сотен лет](https://ru.wikipedia.org/wiki/%D0%98%D1%81%D1%82%D0%BE%D1%80%D0%B8%D1%8F_%D0%B8%D1%81%D0%BA%D1%83%D1%81%D1%81%D1%82%D0%B2%D0%B5%D0%BD%D0%BD%D0%BE%D0%B3%D0%BE_%D0%B8%D0%BD%D1%82%D0%B5%D0%BB%D0%BB%D0%B5%D0%BA%D1%82%D0%B0): в этой статье рассматриваются исторические интеллектуальные основы идеи "мыслящей машины". - ---- -## Заметные открытия - -- 1763, 1812 [Теорема Байеса](https://ru.wikipedia.org/wiki/%D0%A2%D0%B5%D0%BE%D1%80%D0%B5%D0%BC%D0%B0_%D0%91%D0%B0%D0%B9%D0%B5%D1%81%D0%B0) и ее предшественники. Эта теорема и ее приложения лежат в основе вывода, описывающего вероятность события, происходящего на основе предварительных знаний. -- 1805 [Теория наименьших квадратов](https://ru.wikipedia.org/wiki/%D0%9C%D0%B5%D1%82%D0%BE%D0%B4_%D0%BD%D0%B0%D0%B8%D0%BC%D0%B5%D0%BD%D1%8C%D1%88%D0%B8%D1%85_%D0%BA%D0%B2%D0%B0%D0%B4%D1%80%D0%B0%D1%82%D0%BE%D0%B2) французского математика Адриена-Мари Лежандра. Эта теория, о которой вы узнаете в нашем блоке регрессии, помогает в аппроксимации данных. -- 1913 [Цепи Маркова](https://ru.wikipedia.org/wiki/%D0%A6%D0%B5%D0%BF%D1%8C_%D0%9C%D0%B0%D1%80%D0%BA%D0%BE%D0%B2%D0%B0), названный в честь русского математика Андрея Маркова, используется для описания последовательности возможных событий на основе предыдущего состояния. -- 1957 [Персептрон](https://ru.wikipedia.org/wiki/%D0%9F%D0%B5%D1%80%D1%86%D0%B5%D0%BF%D1%82%D1%80%D0%BE%D0%BD) - это тип линейного классификатора, изобретенный американским психологом Фрэнком Розенблаттом, который лежит в основе достижений в области глубокого обучения. - ---- - -- 1967 [Метод ближайшего соседа](https://ru.wikipedia.org/wiki/%D0%91%D0%BB%D0%B8%D0%B6%D0%B0%D0%B9%D1%88%D0%B8%D0%B9_%D1%81%D0%BE%D1%81%D0%B5%D0%B4) - это алгоритм, изначально разработанный для отображения маршрутов. В контексте ML он используется для обнаружения закономерностей. -- 1970 [Обратное распространение ошибки](https://ru.wikipedia.org/wiki/%D0%9C%D0%B5%D1%82%D0%BE%D0%B4_%D0%BE%D0%B1%D1%80%D0%B0%D1%82%D0%BD%D0%BE%D0%B3%D0%BE_%D1%80%D0%B0%D1%81%D0%BF%D1%80%D0%BE%D1%81%D1%82%D1%80%D0%B0%D0%BD%D0%B5%D0%BD%D0%B8%D1%8F_%D0%BE%D1%88%D0%B8%D0%B1%D0%BA%D0%B8) используется для обучения [нейронных сетей с прямой связью](https://ru.wikipedia.org/wiki/%D0%9D%D0%B5%D0%B9%D1%80%D0%BE%D0%BD%D0%BD%D0%B0%D1%8F_%D1%81%D0%B5%D1%82%D1%8C_%D1%81_%D0%BF%D1%80%D1%8F%D0%BC%D0%BE%D0%B9_%D1%81%D0%B2%D1%8F%D0%B7%D1%8C%D1%8E). -- 1982 [Рекуррентные нейронные сети](https://ru.wikipedia.org/wiki/%D0%A0%D0%B5%D0%BA%D1%83%D1%80%D1%80%D0%B5%D0%BD%D1%82%D0%BD%D0%B0%D1%8F_%D0%BD%D0%B5%D0%B9%D1%80%D0%BE%D0%BD%D0%BD%D0%B0%D1%8F_%D1%81%D0%B5%D1%82%D1%8C) являются искусственными нейронными сетями, полученными из нейронных сетей прямой связи, которые создают временные графики. - -✅ Проведите небольшое исследование. Какие еще даты являются ключевыми в истории ML и ИИ? - ---- -## 1950: Машины, которые думают - -Алан Тьюринг, поистине великий человек, который был выбран [общественностью в 2019 году](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) величайшим ученым 20-го века. Считается, что он помог заложить основу концепции "машины, которая может мыслить". Он боролся со скептиками и своей собственной потребностью в эмпирических доказательствах этой концепции, частично создав [Тест Тьюринга](https://www.bbc.com/news/technology-18475646), которые вы изучите на наших уроках NLP. - ---- -## 1956: Летний исследовательский проект в Дартмуте - -"Летний исследовательский проект Дартмута по искусственному интеллекту был основополагающим событием для искусственного интеллекта как области", и именно здесь был придуман термин "искусственный интеллект" ([источник](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)). - -> Всякий аспект обучения или любое другое свойство интеллекта может в принципе быть столь точно описано, что машина сможет его симулировать. - ---- - -Ведущий исследователь, профессор математики Джон Маккарти, надеялся "действовать, основываясь на предположении, что всякий аспект обучения или любое другое свойство интеллекта может в принципе быть столь точно описано, что машина сможет его симулировать". Среди участников был еще один выдающийся ученый в этой области - Марвин Мински. - -Семинару приписывают инициирование и поощрение нескольких дискуссий, в том числе "развитие символических методов, систем, ориентированных на ограниченные области (ранние экспертные системы), и дедуктивных систем по сравнению с индуктивными системами". ([источник](https://ru.wikipedia.org/wiki/%D0%94%D0%B0%D1%80%D1%82%D0%BC%D1%83%D1%82%D1%81%D0%BA%D0%B8%D0%B9_%D1%81%D0%B5%D0%BC%D0%B8%D0%BD%D0%B0%D1%80)). - ---- -## 1956 - 1974: "Золотые годы" - -С 1950-х до середины 70-х годов оптимизм рос в надежде, что ИИ сможет решить многие проблемы. В 1967 году Марвин Мински уверенно заявил, что "В течение одного поколения... проблема создания "искусственного интеллекта" будет в значительной степени решена". (Мински, Марвин (1967), Вычисления: Конечные и бесконечные машины, Энглвуд-Клиффс, Нью-Джерси: Прентис-Холл) - -Исследования в области обработки естественного языка процветали, поиск был усовершенствован и стал более мощным, и была создана концепция "микромиров", где простые задачи выполнялись с использованием простых языковых инструкций. - ---- - -Исследования хорошо финансировались правительственными учреждениями, были достигнуты успехи в вычислениях и алгоритмах, были созданы прототипы интеллектуальных машин. Некоторые из этих машин включают: - -* [Робот Shakey](https://ru.wikipedia.org/wiki/Shakey), который мог маневрировать и решать, как "разумно" выполнять задачи. - - ![Shakey, умный робот](../images/shakey.jpg) - > Shakey в 1972 году - ---- - -* Элиза, ранний "чат-бот", могла общаться с людьми и действовать как примитивный "терапевт". Вы узнаете больше об Элизе на уроках NLP. - - ![Элиза, бот](../images/eliza.png) - > Версия Элизы, чат-бота - ---- - -* "Мир блоков" был примером микромира, в котором блоки можно было складывать и сортировать, а также проводить эксперименты по обучению машин принятию решений. Достижения, созданные с помощью библиотек, таких как [SHRDLU](https://ru.wikipedia.org/wiki/SHRDLU) помогло продвинуть обработку языка вперед. - - [![мир блоков SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "мир блоков SHRDLU") - - > 🎥 Нажмите на изображение выше для просмотра видео: Мир блоков SHRDLU - ---- -## 1974-1980: "Зима искусственного интеллекта" - -К середине 1970-х годов стало очевидно, что сложность создания "интеллектуальных машин" была занижена и что ее перспективы, учитывая доступные вычислительные мощности, были преувеличены. Финансирование иссякло, и доверие к этой области снизилось. Некоторые проблемы, повлиявшие на доверие, включали: ---- -- **Ограничения**. Вычислительная мощность была слишком ограничена. -- **Комбинаторный взрыв**. Количество параметров, необходимых для обучения, росло экспоненциально по мере того, как усложнялись задачи для компьютеров, без параллельной эволюции вычислительной мощности и возможностей. -- **Нехватка данных**. Нехватка данных затрудняла процесс тестирования, разработки и совершенствования алгоритмов. -- **Задаем ли мы правильные вопросы?**. Сами вопросы, которые задавались, начали подвергаться сомнению. Исследователи начали подвергать критике свои подходы: - - Тесты Тьюринга были поставлены под сомнение, среди прочего, с помощью "теории китайской комнаты", которая утверждала, что "программирование цифрового компьютера может создать впечатление, что он понимает язык, но не может обеспечить реальное понимание". ([источник](https://plato.stanford.edu/entries/chinese-room/)) - - Этика внедрения в общество искусственного интеллекта, такого как "терапевт" ЭЛИЗА, была поставлена под сомнение. - ---- - -В то же время начали формироваться различные школы ИИ. Произошло разделение на подходы ["неряшливого" и "чистого" ИИ](https://wikipedia.org/wiki/Neats_and_scruffies). Приверженцы _неряшливого ИИ_ часами корректировали программы, пока не получали желаемых результатов. Приверженцы _Чистого ИИ_ были "сосредоточены на логике и решении формальных задач". ЭЛИЗА и SHRDLU были хорошо известными _неряшливыми_ системами. В 1980-х годах, когда возник спрос на то, чтобы сделать системы машинного обучения воспроизводимыми, _Чистый_ подход постепенно вышел на передний план, поскольку его результаты более объяснимы. - ---- -## Экспертные системы 1980-х годов - -По мере роста отрасли ее преимущества для бизнеса становились все более очевидными, а в 1980-х годах - и распространение "экспертных систем". "Экспертные системы были одними из первых по-настоящему успешных форм программного обеспечения искусственного интеллекта (ИИ)". ([источник](https://ru.wikipedia.org/wiki/%D0%AD%D0%BA%D1%81%D0%BF%D0%B5%D1%80%D1%82%D0%BD%D0%B0%D1%8F_%D1%81%D0%B8%D1%81%D1%82%D0%B5%D0%BC%D0%B0)). - -Этот тип системы на самом деле был _гибридным_, частично состоящим из механизма правил, определяющего бизнес-требования, и механизма вывода, который использует систему правил для вывода новых фактов. - -В эту эпоху также все большее внимания уделялось нейронным сетям. - ---- -## 1987 - 1993: 'Охлаждение' к ИИ - -Распространение специализированного оборудования экспертных систем привело к печальному результату - оно стало слишком специализированным. Появление персональных компьютеров конкурировало с этими крупными специализированными централизованными системами. Началась демократизация вычислительной техники, и в конечном итоге она проложила путь к современному взрыву больших данных. - ---- -## 1993 - 2011 - -Эта эпоха ознаменовала новую эру для ML и ИИ, которые смогли решить некоторые проблемы, возникавшие ранее из-за нехватки данных и вычислительных мощностей. Объем данных начал быстро увеличиваться и становиться все более доступным, и к лучшему и к худшему, особенно с появлением смартфона примерно в 2007 году. Вычислительная мощность росла экспоненциально, и вместе с ней развивались алгоритмы. Эта область начала набирать зрелость по мере того, как свободные дни прошлого начали превращаться в настоящую дисциплину. - ---- -## Сейчас - -Сегодня машинное обучение и искусственный интеллект затрагивают практически все сферы нашей жизни. Текущая эпоха требует тщательного понимания рисков и потенциальных последствий этих алгоритмов для человеческих жизней. Как заявил Брэд Смит из Microsoft, "Информационные технологии поднимают проблемы, которые лежат в основе защиты основных прав человека, таких как конфиденциальность и свобода выражения мнений. Эти проблемы повышают ответственность технологических компаний, которые создают эти продукты. На наш взгляд, они также требуют продуманного государственного регулирования и разработки норм, касающихся приемлемых видов использования" ([источник](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)). - ---- - -Еще предстоит увидеть, что ждет нас в будущем, но важно понимать эти системы, а также программное обеспечение и алгоритмы, которыми они управляют. Мы надеемся, что эта учебная программа поможет вам лучше понять, чтобы вы могли принять решение самостоятельно. - -[![История глубокого обучения](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "История глубокого обучения") -> 🎥 Нажмите на изображение выше, чтобы посмотреть видео: Yann LeCun обсуждает историю глубокого обучения в этой лекции - ---- -## 🚀Вызов - -Погрузитесь в один из этих исторических моментов и узнайте больше о людях, стоящих за ними. Есть увлекательные персонажи, и ни одно научное открытие никогда не создавалось в культурном вакууме. Что вы обнаружите? - -## [Тест после лекции](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/) - ---- -## Обзор и самообучение - -Вот что можно посмотреть и послушать: - -[Этот подкаст, в котором Эми Бойд обсуждает эволюцию ИИ](http://runasradio.com/Shows/Show/739) - -[![История искусственного интеллекта от Эми Бойд](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "История искусственного интеллекта от Эми Бойд") - ---- - -## Задание - -[Создайте временную шкалу](assignment.ru.md) \ No newline at end of file diff --git a/1-Introduction/2-history-of-ML/translations/README.tr.md b/1-Introduction/2-history-of-ML/translations/README.tr.md deleted file mode 100644 index 4145f666..00000000 --- a/1-Introduction/2-history-of-ML/translations/README.tr.md +++ /dev/null @@ -1,117 +0,0 @@ -# Makine öğreniminin tarihi - -![Bir taslak-notta makine öğrenimi geçmişinin özeti](../../../sketchnotes/ml-history.png) -> [Tomomi Imura](https://www.twitter.com/girlie_mac) tarafından hazırlanan taslak-not - -## [Ders öncesi test](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3?loc=tr) - -Bu derste, makine öğrenimi ve yapay zeka tarihindeki önemli kilometre taşlarını inceleyeceğiz. - -Bir alan olarak yapay zekanın (AI) tarihi, makine öğreniminin tarihi ile iç içedir, çünkü makine öğrenimini destekleyen algoritmalar ve bilgi-işlem kapasitesindeki ilerlemeler, yapay zekanın gelişimini beslemektedir. Ayrı bilim alanlanları olarak bu alanlar 1950'lerde belirginleşmeye başlarken, önemli [algoritmik, istatistiksel, matematiksel, hesaplamalı ve teknik keşiflerin](https://wikipedia.org/wiki/Timeline_of_machine_learning) bir kısmı bu dönemden önce gelmiş ve bir kısmı da bu dönem ile örtüşmüştür. Aslında, insanlar [yüzlerce yıldır](https://wikipedia.org/wiki/History_of_artificial_intelligence) bu soruları düşünüyorlar: bu makale bir 'düşünen makine' fikrinin tarihsel entelektüel temellerini tartışıyor. - -## Önemli keşifler - -- 1763, 1812 - [Bayes Teoremi](https://tr.wikipedia.org/wiki/Bayes_teoremi) ve öncülleri. Bu teorem ve uygulamaları, önceki bilgilere dayalı olarak meydana gelen bir olayın olasılığını tanımlayan çıkarımın temelini oluşturur. -- 1805 - [En Küçük Kareler Teorisi](https://tr.wikipedia.org/wiki/En_k%C3%BC%C3%A7%C3%BCk_kareler_y%C3%B6ntemi), Fransız matematikçi Adrien-Marie Legendre tarafından bulunmuştur. Regresyon ünitemizde öğreneceğiniz bu teori, makine öğrenimi modelini veriye uydurmada yardımcı olur. -- 1913 - Rus matematikçi Andrey Markov'un adını taşıyan [Markov Zincirleri](https://tr.wikipedia.org/wiki/Markov_zinciri), önceki bir duruma dayalı olası olaylar dizisini tanımlamak için kullanılır. -- 1957 - [Algılayıcı (Perceptron)](https://tr.wikipedia.org/wiki/Perceptron), derin öğrenmedeki ilerlemelerin temelini oluşturan Amerikalı psikolog Frank Rosenblatt tarafından icat edilen bir tür doğrusal sınıflandırıcıdır. -- 1967 - [En Yakın Komşu](https://wikipedia.org/wiki/Nearest_neighbor), orijinal olarak rotaları haritalamak için tasarlanmış bir algoritmadır. Bir ML bağlamında kalıpları tespit etmek için kullanılır. -- 1970 - [Geri Yayılım](https://wikipedia.org/wiki/Backpropagation), [ileri beslemeli sinir ağlarını](https://wikipedia.org/wiki/Feedforward_neural_network) eğitmek için kullanılır. -- 1982 - [Tekrarlayan Sinir Ağları](https://wikipedia.org/wiki/Recurrent_neural_network), zamansal grafikler oluşturan ileri beslemeli sinir ağlarından türetilen yapay sinir ağlarıdır. - -✅ Biraz araştırma yapın. Makine öğrenimi ve yapay zeka tarihinde önemli olan başka hangi tarihler öne çıkıyor? - -## 1950: Düşünen makineler - -[2019'da halk tarafından](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) 20. yüzyılın en büyük bilim adamı seçilen gerçekten dikkate değer bir kişi olan Alan Turing'in, 'düşünebilen makine' kavramının temellerini attığı kabul edilir. Kendisine karşı çıkanlara yanıt olması için ve bu kavramın deneysel kanıtlarını bulma ihtiyacı sebebiyle, NLP derslerimizde keşfedeceğiniz [Turing Testi'ni](https://www.bbc.com/news/technology-18475646) oluşturdu. - -## 1956: Dartmouth Yaz Araştırma Projesi - -"Yapay zeka üzerine Dartmouth Yaz Araştırma Projesi", bir alan olarak yapay zeka için çığır açan bir olaydı ve burada 'yapay zeka' terimi ortaya çıktı ([kaynak](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)). - -> Öğrenmenin her yönü veya zekanın diğer herhangi bir özelliği, prensipte o kadar kesin bir şekilde tanımlanabilir ki, onu simüle etmek için bir makine yapılabilir. - -Baş araştırmacı, matematik profesörü John McCarthy, "öğrenmenin her yönünün veya zekanın diğer herhangi bir özelliğinin prensipte oldukça kesin bir şekilde tanımlanabileceği varsayımına dayanarak, onu simüle etmek için bir makine yapılabileceği" varsayımının doğru olmasını umarak ilerliyordu. Katılımcılar arasında bu alanın bir diğer önderi olan Marvin Minsky de vardı. - -Çalıştay, "sembolik yöntemlerin yükselişi, sınırlı alanlara odaklanan sistemler (ilk uzman sistemler) ve tümdengelimli sistemlere karşı tümevarımlı sistemler" dahil olmak üzere çeşitli tartışmaları başlatmış ve teşvik etmiştir. ([kaynak](https://tr.wikipedia.org/wiki/Dartmouth_Konferans%C4%B1)). - -## 1956 - 1974: "Altın yıllar" - -1950'lerden 70'lerin ortalarına kadar, yapay zekanın birçok sorunu çözebileceği umuduyla iyimserlik arttı. 1967'de Marvin Minsky kendinden emin bir şekilde "Bir nesil içinde... 'yapay zeka' yaratma sorunu büyük ölçüde çözülecek" dedi. (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall) - -Doğal dil işleme araştırmaları gelişti, aramalar iyileştirildi ve daha güçlü hale getirildi, ve basit görevlerin sade dil talimatları kullanılarak tamamlandığı 'mikro dünyalar' kavramı yaratıldı. - -Araştırmalar, devlet kurumları tarafından iyi finanse edildi, hesaplamalar ve algoritmalarda ilerlemeler kaydedildi ve akıllı makinelerin prototipleri yapıldı. Bu makinelerden bazıları şunlardır: - -* [Robot Shakey](https://wikipedia.org/wiki/Shakey_the_robot), manevra yapabilir ve görevleri 'akıllıca' nasıl yerine getireceğine karar verebilir. - - ![Shakey, akıllı bir robot](../images/shakey.jpg) - > 1972'de Shakey - -* Erken bir 'sohbet botu' olan Eliza, insanlarla sohbet edebilir ve ilkel bir 'terapist' gibi davranabilirdi. NLP derslerinde Eliza hakkında daha fazla bilgi edineceksiniz. - - ![Eliza, bir bot](../images/eliza.png) - > Bir sohbet robotu olan Eliza'nın bir versiyonu - -* "Dünya Blokları", blokların üst üste koyulabilecekleri, sıralanabilecekleri ve karar vermeyi öğreten makinelerdeki deneylerin test edilebileceği bir mikro dünyaya örnekti. [SHRDLU](https://wikipedia.org/wiki/SHRDLU) gibi kütüphanelerle oluşturulan gelişmeler, dil işlemeyi ilerletmeye yardımcı oldu. - - [![SHRDLU ile Dünya Blokları](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "SHRDLU ile Dünya Blokları" ) - - > 🎥 Video için yukarıdaki resme tıklayın: SHRDLU ile Dünya Blokları - -## 1974 - 1980: "Yapay Zekâ Kışı" - -1970'lerin ortalarına gelindiğinde, 'akıllı makineler' yapmanın karmaşıklığının hafife alındığı ve mevcut hesaplama gücü göz önüne alındığında, verilen vaatlerin abartıldığı ortaya çıktı. Finansman kurudu ve alana olan güven azaldı. Güveni etkileyen bazı sorunlar şunlardı: - -- **Kısıtlıklar**. Hesaplama gücü çok sınırlıydı. -- **Kombinasyonel patlama**. Hesaplama gücü ve yeteneğinde paralel bir evrim olmaksızın, bilgisayarlardan daha fazla soru istendikçe, eğitilmesi gereken parametre miktarı katlanarak arttı. -- **Veri eksikliği**. Algoritmaları test etme, geliştirme ve iyileştirme sürecini engelleyen bir veri kıtlığı vardı. -- **Doğru soruları mı soruyoruz?**. Sorulan sorular sorgulanmaya başlandı. Araştırmacılar mevcut yaklaşımları eleştirmeye başladı: - - Turing testleri, diğer fikirlerin yanı sıra, "Çin odası teorisi" aracılığıyla sorgulanmaya başlandı. Bu teori, "dijital bir bilgisayar, programlanarak dili anlıyormuş gibi gösterilebilir fakat gerçek bir dil anlayışı elde edilemez" savını öne sürmektedir. ([kaynak](https://plato.stanford.edu/entries/chinese-room/) - - "Terapist" ELIZA gibi yapay zekaların topluma tanıtılmasının etiğine meydan okundu. - -Aynı zamanda, çeşitli yapay zekâ düşünce okulları oluşmaya başladı. ["dağınık" ile "düzenli AI"](https://wikipedia.org/wiki/Neats_and_scruffies) uygulamaları arasında bir ikilem kuruldu. _Dağınık_ laboratuvarlar, istenen sonuçları elde edene kadar programlar üzerinde saatlerce ince ayar yaptı. _Düzenli_ laboratuvarlar "mantık ve biçimsel problem çözmeye odaklandı". ELIZA ve SHRDLU, iyi bilinen _dağınık_ sistemlerdi. 1980'lerde, ML sistemlerinin sonuçlarını tekrarlanabilir hale getirmek için talep ortaya çıktıkça, sonuçları daha açıklanabilir olduğu için _düzenli_ yaklaşım yavaş yavaş ön plana çıktı. - -## 1980'ler: Uzman sistemler - -Alan büyüdükçe, şirketlere olan faydası daha net hale geldi ve 1980'lerde 'uzman sistemlerin' yaygınlaşması da bu şekilde meydana geldi. "Uzman sistemler, yapay zeka (AI) yazılımlarının gerçek anlamda başarılı olan ilk formları arasındaydı." ([kaynak](https://tr.wikipedia.org/wiki/Uzman_sistemler)). - -Bu sistem türü aslında kısmen iş gereksinimlerini tanımlayan bir kural aracından ve yeni gerçekleri çıkarmak için kurallar sisteminden yararlanan bir çıkarım aracından oluşan bir _melezdir_. - -Bu çağda aynı zamanda sinir ağlarına artan ilgi de görülmüştür. - -## 1987 - 1993: Yapay Zeka 'Soğuması' - -Özelleşmiş uzman sistem donanımının yaygınlaşması, talihsiz bir şekilde bunları aşırı özelleşmiş hale getirdi. Kişisel bilgisayarların yükselişi de bu büyük, özelleşmiş, merkezi sistemlerle rekabet etti. Bilgisayarın demokratikleşmesi başlamıştı ve sonunda modern büyük veri patlamasının yolunu açtı. - -## 1993 - 2011 - -Bu çağ, daha önce veri ve hesaplama gücü eksikliğinden kaynaklanan bazı sorunları çözebilmek için ML ve AI için yeni bir dönemi getirdi. Veri miktarı hızla artmaya başladı ve özellikle 2007'de akıllı telefonun ortaya çıkmasıyla birlikte iyisiyle kötüsüyle daha yaygın bir şekilde ulaşılabilir hale geldi. Hesaplama gücü katlanarak arttı ve algoritmalar da onunla birlikte gelişti. Geçmişin başıboş günleri gitmiş, yerine giderek olgunlaşan gerçek bir disipline dönüşüm başlamıştı. - -## Şimdi - -Günümüzde makine öğrenimi ve yapay zeka hayatımızın neredeyse her alanına dokunuyor. Bu çağ, bu algoritmaların insan yaşamı üzerindeki risklerinin ve potansiyel etkilerinin dikkatli bir şekilde anlaşılmasını gerektirmektedir. Microsoft'tan Brad Smith'in belirttiği gibi, "Bilgi teknolojisi, gizlilik ve ifade özgürlüğü gibi temel insan hakları korumalarının kalbine giden sorunları gündeme getiriyor. Bu sorunlar, bu ürünleri yaratan teknoloji şirketlerinin sorumluluğunu artırıyor. Bizim açımızdan bakıldığında, düşünceli hükümet düzenlemeleri ve kabul edilebilir kullanımlar etrafında normların geliştirilmesi için de bir çağrı niteliği taşıyor." ([kaynak](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/) )). - -Geleceğin neler getireceğini birlikte göreceğiz, ancak bu bilgisayar sistemlerini ve çalıştırdıkları yazılım ve algoritmaları anlamak önemlidir. Bu müfredatın, kendi kararlarınızı verebilmeniz için daha iyi bir anlayış kazanmanıza yardımcı olacağını umuyoruz. - -[![Derin öğrenmenin tarihi](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "Derin öğrenmenin tarihi") -> 🎥 Video için yukarıdaki resme tıklayın: Yann LeCun bu derste derin öğrenmenin tarihini tartışıyor - ---- -## 🚀Meydan okuma - -Bu tarihi anlardan birine girin ve arkasındaki insanlar hakkında daha fazla bilgi edinin. Büyüleyici karakterler var ve kültürel bir boşlukta hiçbir bilimsel keşif yaratılmadı. Ne keşfedersiniz? - -## [Ders sonrası test](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4?loc=tr) - -## İnceleme ve Bireysel Çalışma - -İşte izlenmesi ve dinlenmesi gerekenler: - -[Amy Boyd'un yapay zekanın evrimini tartıştığı bu podcast](http://runasradio.com/Shows/Show/739) - -[![Amy Boyd ile Yapay Zekâ'nın tarihi](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "Amy Boyd ile Yapay Zekâ'nın tarihi") - -## Ödev - -[Bir zaman çizelgesi oluşturun](assignment.tr.md) \ No newline at end of file diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md deleted file mode 100644 index ddd2430d..00000000 --- a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md +++ /dev/null @@ -1,116 +0,0 @@ -# 机器学习的历史 - -![机器学习历史概述](../../../sketchnotes/ml-history.png) -> 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac) - -## [课前测验](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/) - -在本课中，我们将走过机器学习和人工智能历史上的主要里程碑。 - -人工智能（AI）作为一个领域的历史与机器学习的历史交织在一起，因为支持机器学习的算法和计算能力的进步推动了AI的发展。记住，虽然这些领域作为不同研究领域在 20 世纪 50 年代才开始具体化，但重要的[算法、统计、数学、计算和技术发现](https://wikipedia.org/wiki/Timeline_of_machine_learning) 要早于和重叠了这个时代。 事实上，[数百年来](https://wikipedia.org/wiki/History_of_artificial_intelligence)人们一直在思考这些问题：本文讨论了“思维机器”这一概念的历史知识基础。 - -## 主要发现 - -- 1763, 1812 [贝叶斯定理](https://wikipedia.org/wiki/Bayes%27_theorem) 及其前身。该定理及其应用是推理的基础，描述了基于先验知识的事件发生的概率。 -- 1805 [最小二乘理论](https://wikipedia.org/wiki/Least_squares)由法国数学家 Adrien-Marie Legendre 提出。 你将在我们的回归单元中了解这一理论，它有助于数据拟合。 -- 1913 [马尔可夫链](https://wikipedia.org/wiki/Markov_chain)以俄罗斯数学家 Andrey Markov 的名字命名，用于描述基于先前状态的一系列可能事件。 -- 1957 [感知器](https://wikipedia.org/wiki/Perceptron)是美国心理学家 Frank Rosenblatt 发明的一种线性分类器，是深度学习发展的基础。 -- 1967 [最近邻](https://wikipedia.org/wiki/Nearest_neighbor)是一种最初设计用于映射路线的算法。 在 ML 中，它用于检测模式。 -- 1970 [反向传播](https://wikipedia.org/wiki/Backpropagation)用于训练[前馈神经网络](https://wikipedia.org/wiki/Feedforward_neural_network)。 -- 1982 [循环神经网络](https://wikipedia.org/wiki/Recurrent_neural_network) 是源自产生时间图的前馈神经网络的人工神经网络。 - -✅ 做点调查。在 ML 和 AI 的历史上，还有哪些日期是重要的？ -## 1950: 会思考的机器 - -Alan Turing，一个真正杰出的人，[在 2019 年被公众投票选出](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) 作为 20 世纪最伟大的科学家，他认为有助于为“会思考的机器”的概念打下基础。他通过创建 [图灵测试](https://www.bbc.com/news/technology-18475646)来解决反对者和他自己对这一概念的经验证据的需求，你将在我们的 NLP 课程中进行探索。 - -## 1956: 达特茅斯夏季研究项目 - -“达特茅斯夏季人工智能研究项目是人工智能领域的一个开创性事件，”正是在这里，人们创造了“人工智能”一词（[来源](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)）。 - -> 原则上，学习的每个方面或智能的任何其他特征都可以被精确地描述，以至于可以用机器来模拟它。 - -首席研究员、数学教授 John McCarthy 希望“基于这样一种猜想，即学习的每个方面或智能的任何其他特征原则上都可以如此精确地描述，以至于可以制造出一台机器来模拟它。” 参与者包括该领域的另一位杰出人物 Marvin Minsky。 - -研讨会被认为发起并鼓励了一些讨论，包括“符号方法的兴起、专注于有限领域的系统（早期专家系统），以及演绎系统与归纳系统的对比。”（[来源](https://wikipedia.org/wiki/Dartmouth_workshop)）。 - -## 1956 - 1974: “黄金岁月” - -从 20 世纪 50 年代到 70 年代中期，乐观情绪高涨，希望人工智能能够解决许多问题。1967 年，Marvin Minsky 自信地说，“一代人之内...创造‘人工智能’的问题将得到实质性的解决。”（Minsky，Marvin（1967），《计算：有限和无限机器》，新泽西州恩格伍德克利夫斯：Prentice Hall） - -自然语言处理研究蓬勃发展，搜索被提炼并变得更加强大，创造了“微观世界”的概念，在这个概念中，简单的任务是用简单的语言指令完成的。 - -这项研究得到了政府机构的充分资助，在计算和算法方面取得了进展，并建造了智能机器的原型。其中一些机器包括： - -* [机器人 Shakey](https://wikipedia.org/wiki/Shakey_the_robot)，他们可以“聪明地”操纵和决定如何执行任务。 - - ![Shakey, 智能机器人](../images/shakey.jpg) - > 1972 年的 Shakey - -* Eliza，一个早期的“聊天机器人”，可以与人交谈并充当原始的“治疗师”。 你将在 NLP 课程中了解有关 Eliza 的更多信息。 - - ![Eliza, 机器人](../images/eliza.png) - > Eliza 的一个版本，一个聊天机器人 - -* “积木世界”是一个微观世界的例子，在那里积木可以堆叠和分类，并且可以测试教机器做出决策的实验。 使用 [SHRDLU](https://wikipedia.org/wiki/SHRDLU) 等库构建的高级功能有助于推动语言处理向前发展。 - - [![积木世界与 SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "积木世界与SHRDLU") - - > 🎥 点击上图观看视频： 积木世界与 SHRDLU - -## 1974 - 1980: AI 的寒冬 - -到了 20 世纪 70 年代中期，很明显制造“智能机器”的复杂性被低估了，而且考虑到可用的计算能力，它的前景被夸大了。资金枯竭，市场信心放缓。影响信心的一些问题包括： - -- **限制**。计算能力太有限了 -- **组合爆炸**。随着对计算机的要求越来越高，需要训练的参数数量呈指数级增长，而计算能力却没有平行发展。 -- **缺乏数据**。 缺乏数据阻碍了测试、开发和改进算法的过程。 -- **我们是否在问正确的问题？**。 被问到的问题也开始受到质疑。 研究人员开始对他们的方法提出批评： - - 图灵测试受到质疑的方法之一是“中国房间理论”，该理论认为，“对数字计算机进行编程可能使其看起来能理解语言，但不能产生真正的理解。” ([来源](https://plato.stanford.edu/entries/chinese-room/)) - - 将“治疗师”ELIZA 这样的人工智能引入社会的伦理受到了挑战。 - -与此同时，各种人工智能学派开始形成。 在 [“scruffy” 与 “neat AI”](https://wikipedia.org/wiki/Neats_and_scruffies) 之间建立了二分法。 _Scruffy_ 实验室对程序进行了数小时的调整，直到获得所需的结果。 _Neat_ 实验室“专注于逻辑和形式问题的解决”。 ELIZA 和 SHRDLU 是众所周知的 _scruffy_ 系统。 在 1980 年代，随着使 ML 系统可重现的需求出现，_neat_ 方法逐渐走上前沿，因为其结果更易于解释。 - -## 1980s 专家系统 - -随着这个领域的发展，它对商业的好处变得越来越明显，在 20 世纪 80 年代，‘专家系统’也开始广泛流行起来。“专家系统是首批真正成功的人工智能 (AI) 软件形式之一。” （[来源](https://wikipedia.org/wiki/Expert_system)）。 - -这种类型的系统实际上是混合系统，部分由定义业务需求的规则引擎和利用规则系统推断新事实的推理引擎组成。 - -在这个时代，神经网络也越来越受到重视。 - -## 1987 - 1993: AI 的冷静期 - -专业的专家系统硬件的激增造成了过于专业化的不幸后果。个人电脑的兴起也与这些大型、专业化、集中化系统展开了竞争。计算机的平民化已经开始，它最终为大数据的现代爆炸铺平了道路。 - -## 1993 - 2011 - -这个时代见证了一个新的时代，ML 和 AI 能够解决早期由于缺乏数据和计算能力而导致的一些问题。数据量开始迅速增加，变得越来越广泛，无论好坏，尤其是 2007 年左右智能手机的出现，计算能力呈指数级增长，算法也随之发展。这个领域开始变得成熟，因为过去那些随心所欲的日子开始具体化为一种真正的纪律。 - -## 现在 - -今天，机器学习和人工智能几乎触及我们生活的每一个部分。这个时代要求仔细了解这些算法对人类生活的风险和潜在影响。正如微软的 Brad Smith 所言，“信息技术引发的问题触及隐私和言论自由等基本人权保护的核心。这些问题加重了制造这些产品的科技公司的责任。在我们看来，它们还呼吁政府进行深思熟虑的监管，并围绕可接受的用途制定规范”（[来源](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)）。 - -未来的情况还有待观察，但了解这些计算机系统以及它们运行的软件和算法是很重要的。我们希望这门课程能帮助你更好的理解，以便你自己决定。 - -[![深度学习的历史](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "深度学习的历史") -> 🎥 点击上图观看视频：Yann LeCun 在本次讲座中讨论深度学习的历史 - ---- -## 🚀挑战 - -深入了解这些历史时刻之一，并更多地了解它们背后的人。这里有许多引人入胜的人物，没有一项科学发现是在文化真空中创造出来的。你发现了什么？ - -## [课后测验](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/) - -## 复习与自学 - -以下是要观看和收听的节目： - -[这是 Amy Boyd 讨论人工智能进化的播客](http://runasradio.com/Shows/Show/739) - -[![Amy Boyd的《人工智能史》](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "Amy Boyd的《人工智能史》") - -## 任务 - -[创建时间线](assignment.zh-cn.md) diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-tw.md b/1-Introduction/2-history-of-ML/translations/README.zh-tw.md deleted file mode 100644 index 7c88c2ee..00000000 --- a/1-Introduction/2-history-of-ML/translations/README.zh-tw.md +++ /dev/null @@ -1,110 +0,0 @@ -# 機器學習的歷史 - -![機器學習歷史概述](../../../sketchnotes/ml-history.png) -> 作者 [Tomomi Imura](https://www.twitter.com/girlie_mac) -## [課前測驗](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/3/) - -在本課中，我們將走過機器學習和人工智能歷史上的主要裏程碑。 - -人工智能（AI）作為一個領域的歷史與機器學習的歷史交織在一起，因為支持機器學習的算法和計算能力的進步推動了AI的發展。記住，雖然這些領域作為不同研究領域在 20 世紀 50 年代才開始具體化，但重要的[算法、統計、數學、計算和技術發現](https://wikipedia.org/wiki/Timeline_of_machine_learning) 要早於和重疊了這個時代。 事實上，[數百年來](https://wikipedia.org/wiki/History_of_artificial_intelligence)人們一直在思考這些問題：本文討論了「思維機器」這一概念的歷史知識基礎。 - -## 主要發現 - -- 1763, 1812 [貝葉斯定理](https://wikipedia.org/wiki/Bayes%27_theorem) 及其前身。該定理及其應用是推理的基礎，描述了基於先驗知識的事件發生的概率。 -- 1805 [最小二乘理論](https://wikipedia.org/wiki/Least_squares)由法國數學家 Adrien-Marie Legendre 提出。 你將在我們的回歸單元中了解這一理論，它有助於數據擬合。 -- 1913 [馬爾可夫鏈](https://wikipedia.org/wiki/Markov_chain)以俄羅斯數學家 Andrey Markov 的名字命名，用於描述基於先前狀態的一系列可能事件。 -- 1957 [感知器](https://wikipedia.org/wiki/Perceptron)是美國心理學家 Frank Rosenblatt 發明的一種線性分類器，是深度學習發展的基礎。 -- 1967 [最近鄰](https://wikipedia.org/wiki/Nearest_neighbor)是一種最初設計用於映射路線的算法。 在 ML 中，它用於檢測模式。 -- 1970 [反向傳播](https://wikipedia.org/wiki/Backpropagation)用於訓練[前饋神經網絡](https://wikipedia.org/wiki/Feedforward_neural_network)。 -- 1982 [循環神經網絡](https://wikipedia.org/wiki/Recurrent_neural_network) 是源自產生時間圖的前饋神經網絡的人工神經網絡。 - -✅ 做點調查。在 ML 和 AI 的歷史上，還有哪些日期是重要的？ -## 1950: 會思考的機器 - -Alan Turing，一個真正傑出的人，[在 2019 年被公眾投票選出](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) 作為 20 世紀最偉大的科學家，他認為有助於為「會思考的機器」的概念打下基礎。他通過創建 [圖靈測試](https://www.bbc.com/news/technology-18475646)來解決反對者和他自己對這一概念的經驗證據的需求，你將在我們的 NLP 課程中進行探索。 - -## 1956: 達特茅斯夏季研究項目 - -「達特茅斯夏季人工智能研究項目是人工智能領域的一個開創性事件，」正是在這裏，人們創造了「人工智能」一詞（[來源](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)）。 - -> 原則上，學習的每個方面或智能的任何其他特征都可以被精確地描述，以至於可以用機器來模擬它。 -首席研究員、數學教授 John McCarthy 希望「基於這樣一種猜想，即學習的每個方面或智能的任何其他特征原則上都可以如此精確地描述，以至於可以製造出一臺機器來模擬它。」 參與者包括該領域的另一位傑出人物 Marvin Minsky。 - -研討會被認為發起並鼓勵了一些討論，包括「符號方法的興起、專註於有限領域的系統（早期專家系統），以及演繹系統與歸納系統的對比。」（[來源](https://wikipedia.org/wiki/Dartmouth_workshop)）。 - -## 1956 - 1974: 「黃金歲月」 - -從 20 世紀 50 年代到 70 年代中期，樂觀情緒高漲，希望人工智能能夠解決許多問題。1967 年，Marvin Minsky 自信地說，「一代人之內...創造『人工智能』的問題將得到實質性的解決。」（Minsky，Marvin（1967），《計算：有限和無限機器》，新澤西州恩格伍德克利夫斯：Prentice Hall） - -自然語言處理研究蓬勃發展，搜索被提煉並變得更加強大，創造了「微觀世界」的概念，在這個概念中，簡單的任務是用簡單的語言指令完成的。 - -這項研究得到了政府機構的充分資助，在計算和算法方面取得了進展，並建造了智能機器的原型。其中一些機器包括： - -* [機器人 Shakey](https://wikipedia.org/wiki/Shakey_the_robot)，他們可以「聰明地」操縱和決定如何執行任務。 - - ![Shakey, 智能機器人](../images/shakey.jpg) - > 1972 年的 Shakey -* Eliza，一個早期的「聊天機器人」，可以與人交談並充當原始的「治療師」。 你將在 NLP 課程中了解有關 Eliza 的更多信息。 - - ![Eliza, 機器人](../images/eliza.png) - > Eliza 的一個版本，一個聊天機器人 -* 「積木世界」是一個微觀世界的例子，在那裏積木可以堆疊和分類，並且可以測試教機器做出決策的實驗。 使用 [SHRDLU](https://wikipedia.org/wiki/SHRDLU) 等庫構建的高級功能有助於推動語言處理向前發展。 - - [![積木世界與 SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "積木世界與SHRDLU") - - > 🎥 點擊上圖觀看視頻： 積木世界與 SHRDLU -## 1974 - 1980: AI 的寒冬 - -到了 20 世紀 70 年代中期，很明顯製造「智能機器」的復雜性被低估了，而且考慮到可用的計算能力，它的前景被誇大了。資金枯竭，市場信心放緩。影響信心的一些問題包括： - -- **限製**。計算能力太有限了 -- **組合爆炸**。隨著對計算機的要求越來越高，需要訓練的參數數量呈指數級增長，而計算能力卻沒有平行發展。 -- **缺乏數據**。 缺乏數據阻礙了測試、開發和改進算法的過程。 -- **我們是否在問正確的問題？**。 被問到的問題也開始受到質疑。 研究人員開始對他們的方法提出批評： - - 圖靈測試受到質疑的方法之一是「中國房間理論」，該理論認為，「對數字計算機進行編程可能使其看起來能理解語言，但不能產生真正的理解。」 ([來源](https://plato.stanford.edu/entries/chinese-room/)) - - 將「治療師」ELIZA 這樣的人工智能引入社會的倫理受到了挑戰。 - -與此同時，各種人工智能學派開始形成。 在 [「scruffy」 與 「neat AI」](https://wikipedia.org/wiki/Neats_and_scruffies) 之間建立了二分法。 _Scruffy_ 實驗室對程序進行了數小時的調整，直到獲得所需的結果。 _Neat_ 實驗室「專註於邏輯和形式問題的解決」。 ELIZA 和 SHRDLU 是眾所周知的 _scruffy_ 系統。 在 1980 年代，隨著使 ML 系統可重現的需求出現，_neat_ 方法逐漸走上前沿，因為其結果更易於解釋。 - -## 1980s 專家系統 - -隨著這個領域的發展，它對商業的好處變得越來越明顯，在 20 世紀 80 年代，『專家系統』也開始廣泛流行起來。「專家系統是首批真正成功的人工智能 (AI) 軟件形式之一。」 （[來源](https://wikipedia.org/wiki/Expert_system)）。 - -這種類型的系統實際上是混合系統，部分由定義業務需求的規則引擎和利用規則系統推斷新事實的推理引擎組成。 - -在這個時代，神經網絡也越來越受到重視。 - -## 1987 - 1993: AI 的冷靜期 - -專業的專家系統硬件的激增造成了過於專業化的不幸後果。個人電腦的興起也與這些大型、專業化、集中化系統展開了競爭。計算機的平民化已經開始，它最終為大數據的現代爆炸鋪平了道路。 - -## 1993 - 2011 - -這個時代見證了一個新的時代，ML 和 AI 能夠解決早期由於缺乏數據和計算能力而導致的一些問題。數據量開始迅速增加，變得越來越廣泛，無論好壞，尤其是 2007 年左右智能手機的出現，計算能力呈指數級增長，算法也隨之發展。這個領域開始變得成熟，因為過去那些隨心所欲的日子開始具體化為一種真正的紀律。 - -## 現在 - -今天，機器學習和人工智能幾乎觸及我們生活的每一個部分。這個時代要求仔細了解這些算法對人類生活的風險和潛在影響。正如微軟的 Brad Smith 所言，「信息技術引發的問題觸及隱私和言論自由等基本人權保護的核心。這些問題加重了製造這些產品的科技公司的責任。在我們看來，它們還呼籲政府進行深思熟慮的監管，並圍繞可接受的用途製定規範」（[來源](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)）。 - -未來的情況還有待觀察，但了解這些計算機系統以及它們運行的軟件和算法是很重要的。我們希望這門課程能幫助你更好的理解，以便你自己決定。 - -[![深度學習的歷史](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "深度學習的歷史") -> 🎥 點擊上圖觀看視頻：Yann LeCun 在本次講座中討論深度學習的歷史 ---- -## 🚀挑戰 - -深入了解這些歷史時刻之一，並更多地了解它們背後的人。這裏有許多引人入勝的人物，沒有一項科學發現是在文化真空中創造出來的。你發現了什麽？ - -## [課後測驗](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/4/) - -## 復習與自學 - -以下是要觀看和收聽的節目： - -[這是 Amy Boyd 討論人工智能進化的播客](http://runasradio.com/Shows/Show/739) - -[![Amy Boyd的《人工智能史》](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "Amy Boyd的《人工智能史》") - -## 任務 - -[創建時間線](assignment.zh-tw.md) \ No newline at end of file diff --git a/1-Introduction/2-history-of-ML/translations/assignment.es.md b/1-Introduction/2-history-of-ML/translations/assignment.es.md deleted file mode 100644 index 504796e7..00000000 --- a/1-Introduction/2-history-of-ML/translations/assignment.es.md +++ /dev/null @@ -1,11 +0,0 @@ -# Crea una línea de tiempo - -## Instrucciones - -Usando [este repositorio](https://github.com/Digital-Humanities-Toolkit/timeline-builder), crea una línea temporal de algunos aspectos de la historia de los algoritmos, matemáticas, estadística, Inteligencia Artificial (AI), Aprendizaje Automático (ML), o una combinación de todos estos. Te puedes enfocar en una persona, una idea o período largo de tiempo de pensamiento. Asegúrate de agregar elementos multimedia. - -## Rúbrica - -| Criterio | Ejemplar | Adecuado | Necesita mejorar | -| -------- | ------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------- | -| | Una línea de tiempo desplegada es representada como una página de Github | El código está incompleto y no fue desplegado | La línea del tiempo está incompleta, sin buena investigación y sin desplegar | diff --git a/1-Introduction/2-history-of-ML/translations/assignment.fr.md b/1-Introduction/2-history-of-ML/translations/assignment.fr.md deleted file mode 100644 index c562516e..00000000 --- a/1-Introduction/2-history-of-ML/translations/assignment.fr.md +++ /dev/null @@ -1,11 +0,0 @@ -# Créer une frise chronologique - -## Instructions - -Utiliser [ce repo](https://github.com/Digital-Humanities-Toolkit/timeline-builder), créer une frise chronologique de certains aspects de l'histoire des algorithmes, des mathématiques, des statistiques, de l'IA ou du machine learning, ou une combinaison de ceux-ci. Vous pouvez vous concentrer sur une personne, une idée ou une longue période d'innovations. Assurez-vous d'ajouter des éléments multimédias. - -## Rubrique - -| Critères | Exemplaire | Adéquate | A améliorer | -| -------- | ---------------------------------------------------------------- | ------------------------------------ | ------------------------------------------------------------------ | -| | Une chronologie déployée est présentée sous forme de page GitHub | Le code est incomplet et non déployé | La chronologie est incomplète, pas bien recherchée et pas déployée | diff --git a/1-Introduction/2-history-of-ML/translations/assignment.id.md b/1-Introduction/2-history-of-ML/translations/assignment.id.md deleted file mode 100644 index 0ee7c009..00000000 --- a/1-Introduction/2-history-of-ML/translations/assignment.id.md +++ /dev/null @@ -1,11 +0,0 @@ -# Membuat sebuah *timeline* - -## Instruksi - -Menggunakan [repo ini](https://github.com/Digital-Humanities-Toolkit/timeline-builder), buatlah sebuah *timeline* dari beberapa aspek sejarah algoritma, matematika, statistik, AI, atau ML, atau kombinasi dari semuanya. Kamu dapat fokus pada satu orang, satu ide, atau rentang waktu pemikiran yang panjang. Pastikan untuk menambahkan elemen multimedia. - -## Rubrik - -| Kriteria | Sangat Bagus | Cukup | Perlu Peningkatan | -| -------- | ------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------- | -| | *Timeline* yang dideploy disajikan sebagai halaman GitHub | Kode belum lengkap dan belum dideploy | *Timeline* belum lengkap, belum diriset dengan baik dan belum dideploy | \ No newline at end of file diff --git a/1-Introduction/2-history-of-ML/translations/assignment.it.md b/1-Introduction/2-history-of-ML/translations/assignment.it.md deleted file mode 100644 index 4de7ed14..00000000 --- a/1-Introduction/2-history-of-ML/translations/assignment.it.md +++ /dev/null @@ -1,11 +0,0 @@ -# Creare una sequenza temporale - -## Istruzioni - -Usando [questo repository](https://github.com/Digital-Humanities-Toolkit/timeline-builder), si crei una sequenza temporale di alcuni aspetti della storia di algoritmi, matematica, statistica, AI o ML, o una combinazione di questi. Ci si può concentrare su una persona, un'idea o un lungo lasso di tempo di pensiero. Ci si assicuri di aggiungere elementi multimediali. - -## Rubrica - -| Criteri | Ottimo | Adeguato | Necessita miglioramento | -| -------- | ------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------- | -| | Una sequenza temporale distribuita viene presentata come una pagina GitHub | Il codice è incompleto e non è stato distribuito | La sequenza temporale è incompleta, non ben studiata e non implementata | diff --git a/1-Introduction/2-history-of-ML/translations/assignment.ja.md b/1-Introduction/2-history-of-ML/translations/assignment.ja.md deleted file mode 100644 index f5f78799..00000000 --- a/1-Introduction/2-history-of-ML/translations/assignment.ja.md +++ /dev/null @@ -1,11 +0,0 @@ -# 年表を作成する - -## 指示 - -[このリポジトリ](https://github.com/Digital-Humanities-Toolkit/timeline-builder) を使って、アルゴリズム・数学・統計学・人工知能・機械学習、またはこれらの組み合わせに対して、歴史のひとつの側面に関する年表を作成してください。焦点を当てるのは、ひとりの人物・ひとつのアイディア・長期間にわたる思想のいずれのものでも構いません。マルチメディアの要素を必ず加えるようにしてください。 - -## 評価基準 - -| 基準 | 模範的 | 十分 | 要改善 | -| ---- | -------------------------------------- | ------------------------------------ | ------------------------------------------------------------ | -| | GitHub page に年表がデプロイされている | コードが未完成でデプロイされていない | 年表が未完成で、十分に調査されておらず、デプロイされていない | diff --git a/1-Introduction/2-history-of-ML/translations/assignment.ko.md b/1-Introduction/2-history-of-ML/translations/assignment.ko.md deleted file mode 100644 index 7d3d2fa7..00000000 --- a/1-Introduction/2-history-of-ML/translations/assignment.ko.md +++ /dev/null @@ -1,11 +0,0 @@ -# 타임라인을 만들어 봅시다 - -## 설명 - -이 [저장소(repository)](https://github.com/Digital-Humanities-Toolkit/timeline-builder)를 사용해 알고리즘, 수학, 통계, 인공지능 또는 머신러닝 중 한 가지 이상의 연혁사를 조합하여 타임라인을 만들어 보세요. 한 명의 인물 또는 한 가지 아이디어를 집중적으로 다루셔도 되고, 여러 세기에 걸친 긴 연혁사를 다루시는 것도 좋습니다. 각 사건과 관련된 멀티미디어도 타임라인에 추가해 보시기 바랍니다. - -## 평가기준표 - -| 평가기준 | 모범 | 적절 | 향상 필요 | -| -------- | ----------------------------------------- | -------------------- | --------------------------------- | -| | 깃허브(GitHub) 페이지에 완성된 타임라인 공개 | 코드 미완성 및 미배포 | 타임라인 조사 미흡, 미완성 및 미배포 | diff --git a/1-Introduction/2-history-of-ML/translations/assignment.pt-br.md b/1-Introduction/2-history-of-ML/translations/assignment.pt-br.md deleted file mode 100644 index 9e1e6ce1..00000000 --- a/1-Introduction/2-history-of-ML/translations/assignment.pt-br.md +++ /dev/null @@ -1,11 +0,0 @@ -# Crie uma linha do tempo - -## Instruções - -Usando [este repositório](https://github.com/Digital-Humanities-Toolkit/timeline-builder), crie uma linha do tempo de algum aspecto da história de algoritmos, matemática, estatística, AI ou ML, ou uma combinação de esses. Você pode se concentrar em uma pessoa, uma ideia ou um longo período de pensamento. Certifique-se de adicionar elementos de multimídia. - -## Rubrica - -| Critérios | Exemplar | Adequado | Precisa Melhorar | -| -------- | ------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------- | -| | Um cronograma implantado é apresentado como uma página do GitHub (GitHub Page) | O código está incompleto e não implementado | O cronograma está incompleto, não foi bem pesquisado e não implantado | \ No newline at end of file diff --git a/1-Introduction/2-history-of-ML/translations/assignment.ru.md b/1-Introduction/2-history-of-ML/translations/assignment.ru.md deleted file mode 100644 index 7ee1b620..00000000 --- a/1-Introduction/2-history-of-ML/translations/assignment.ru.md +++ /dev/null @@ -1,11 +0,0 @@ -# Создайте временную шкалу - -## Инструкции - -Используя [этот репозиторий](https://github.com/Digital-Humanities-Toolkit/timeline-builder), создайте временную шкалу какого-либо аспекта истории алгоритмов, математики, статистики, искусственного интеллекта или ML или их комбинации. Вы можете сосредоточиться на одном человеке, одной идее или на длительном промежутке времени. Обязательно добавьте мультимедийные элементы. - -## Рубрика - -| Критерии | Образцовый | Адекватный | Нуждается в улучшении | -| -------- | ------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------- | -| | Развернутая временная шкала представлена в виде страницы GitHub | Код неполон и не развернут | Временная шкала неполная, недостаточно изучена и не развернута | \ No newline at end of file diff --git a/1-Introduction/2-history-of-ML/translations/assignment.tr.md b/1-Introduction/2-history-of-ML/translations/assignment.tr.md deleted file mode 100644 index f0e87763..00000000 --- a/1-Introduction/2-history-of-ML/translations/assignment.tr.md +++ /dev/null @@ -1,11 +0,0 @@ -# Bir zaman çizelgesi oluşturun - -## Talimatlar - -[Bu repoyu](https://github.com/Digital-Humanities-Toolkit/timeline-builder) kullanarak; algoritmaların, matematiğin, istatistiğin, AI veya ML'in veya bunların bir kombinasyonunun tarihinin bazı yönlerinin bir zaman çizelgesini oluşturun. Bir kişiye, bir fikre veya bir düşüncenin uzun bir zamanına odaklanabilirsiniz. Multimedya öğeleri eklediğinizden emin olun. - -## Değerlendirme Listesi - -| | Takdir edilesi | Yeterli | İyileştirilmesi Lazım | -| -------- | ------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------- | -| Kriterler | Zaman çizelgesi bir GitHub sayfası olarak yayınlanmış | Kod eksik ve henüz yayınlanmamış | Zaman çizelgesi eksik, iyi araştırılmamış ve yayınlanmamış | \ No newline at end of file diff --git a/1-Introduction/2-history-of-ML/translations/assignment.zh-cn.md b/1-Introduction/2-history-of-ML/translations/assignment.zh-cn.md deleted file mode 100644 index adf3ee15..00000000 --- a/1-Introduction/2-history-of-ML/translations/assignment.zh-cn.md +++ /dev/null @@ -1,11 +0,0 @@ -# 建立一个时间轴 - -## 说明 - -使用这个 [仓库](https://github.com/Digital-Humanities-Toolkit/timeline-builder)，创建一个关于算法、数学、统计学、人工智能、机器学习的某个方面或者可以综合多个以上学科来讲。你可以着重介绍某个人，某个想法，或者一个经久不衰的思想。请确保添加了多媒体元素在你的时间线中。 - -## 评判标准 - -| 标准 | 优秀 | 中规中矩 | 仍需努力 | -| ------------ | ---------------------------------- | ---------------------- | ------------------------------------------ | -| | 有一个用 GitHub page 展示的 timeline | 代码还不完整并且没有部署 | 时间线不完整，没有经过充分的研究，并且没有部署 | diff --git a/1-Introduction/2-history-of-ML/translations/assignment.zh-tw.md b/1-Introduction/2-history-of-ML/translations/assignment.zh-tw.md deleted file mode 100644 index f372e6ae..00000000 --- a/1-Introduction/2-history-of-ML/translations/assignment.zh-tw.md +++ /dev/null @@ -1,12 +0,0 @@ - -# 建立一個時間軸 - -## 說明 - -使用這個 [倉庫](https://github.com/Digital-Humanities-Toolkit/timeline-builder)，創建一個關於算法、數學、統計學、人工智能、機器學習的某個方面或者可以綜合多個以上學科來講。你可以著重介紹某個人，某個想法，或者一個經久不衰的思想。請確保添加了多媒體元素在你的時間線中。 - -## 評判標準 - -| 標準 | 優秀 | 中規中矩 | 仍需努力 | -| ------------ | ---------------------------------- | ---------------------- | ------------------------------------------ | -| | 有一個用 GitHub page 展示的 timeline | 代碼還不完整並且沒有部署 | 時間線不完整，沒有經過充分的研究，並且沒有部署 | \ No newline at end of file diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md deleted file mode 100644 index 240181a8..00000000 --- a/1-Introduction/3-fairness/README.md +++ /dev/null @@ -1,158 +0,0 @@ -# Building Machine Learning solutions with responsible AI - -![Summary of responsible AI in Machine Learning in a sketchnote](../../sketchnotes/ml-fairness.png) -> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac) - -## [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/5/) - -## Introduction - -In this curriculum, you will start to discover how machine learning can and is impacting our everyday lives. Even now, systems and models are involved in daily decision-making tasks, such as health care diagnoses, loan approvals or detecting fraud. So, it is important that these models work well to provide outcomes that are trustworthy. Just as any software application, AI systems are going to miss expectations or have an undesirable outcome. That is why it is essential to be about to understand and explain the behavior of an AI model. - -Imagine what can happen when the data you are using to build these models lacks certain demographics, such as race, gender, political view, religion, or disproportionally represents such demographics. What about when the model’s output is interpreted to favor some demographic? What is the consequence for the application? In addition, what happens when the model has an adverse outcome and is harmful to people? Who is accountable for the AI systems behavior? These are some questions we will explore in this curriculum. - -In this lesson, you will: - -- Raise your awareness of the importance of fairness in machine learning and fairness-related harms. -- Become familiar with the practice of exploring outliers and unusual scenarios to ensure reliability and safety -- Gain understanding on the need to empower everyone by designing inclusive systems -- Explore how vital it is to protect privacy and security of data and people -- See the importance of having a glass box approach to explain the behavior of AI models -- Be mindful of how accountability is essential to build trust in AI systems - -## Prerequisite - -As a prerequisite, please take the "Responsible AI Principles" Learn Path and watch the video below on the topic: - -Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-77952-leestott) - -[![Microsoft's Approach to Responsible AI](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI") - -> 🎥 Click the image above for a video: Microsoft's Approach to Responsible AI - -## Fairness - -AI systems should treat everyone fairly and avoid affecting similar groups of people in different ways. For example, when AI systems provide guidance on medical treatment, loan applications, or employment, they should make the same recommendations to everyone with similar symptoms, financial circumstances, or professional qualifications. Each of us as humans carries around inherited biases that affect our decisions and actions. These biases can be evident in the data that we use to train AI systems. Such manipulation can sometimes happen unintentionally. It is often difficult to consciously know when you are introducing bias in data. - -**“Unfairness”** encompasses negative impacts, or “harms”, for a group of people, such as those defined in terms of race, gender, age, or disability status. The main fairness-related harms can be classified as: - -- **Allocation**, if a gender or ethnicity for example is favored over another. -- **Quality of service**. If you train the data for one specific scenario but reality is much more complex, it leads to a poor performing service. For instance, a hand soap dispenser that could not seem to be able to sense people with dark skin. [Reference](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773) -- **Denigration**. To unfairly criticize and label something or someone. For example, an image labeling technology infamously mislabeled images of dark-skinned people as gorillas. -- **Over- or under- representation**. The idea is that a certain group is not seen in a certain profession, and any service or function that keeps promoting that is contributing to harm. -- **Stereotyping**. Associating a given group with pre-assigned attributes. For example, a language translation system betweem English and Turkish may have inaccuraces due to words with stereotypical associations to gender. - -![translation to Turkish](images/gender-bias-translate-en-tr.png) -> translation to Turkish - -![translation back to English](images/gender-bias-translate-tr-en.png) -> translation back to English - -When designing and testing AI systems, we need to ensure that AI is fair and not programmed to make biased or discriminatory decisions, which human beings are also prohibited from making. Guaranteeing fairness in AI and machine learning remains a complex sociotechnical challenge. - -### Reliability and safety - -To build trust, AI systems need to be reliable, safe, and consistent under normal and unexpected conditions. It is important to know how AI systems will behavior in a variety of situations, especially when they are outliers. When building AI solutions, there needs to be a substantial amount of focus on how to handle a wide variety of circumstances that the AI solutions would encounter. For example, a self-driving car needs to put people's safety as a top priority. As a result, the AI powering the car need to consider all the possible scenarios that the car could come across such as night, thunderstorms or blizzards, kids running across the street, pets, road constructions etc. How well an AI system can handle a wild range of conditions reliably and safely reflects the level of anticipation the data scientist or AI developer considered during the design or testing of the system. - -> [🎥 Click the here for a video: ](https://www.microsoft.com/videoplayer/embed/RE4vvIl) - -### Inclusiveness - -AI systems should be designed to engage and empower everyone. When designing and implementing AI systems data scientists and AI developers identify and address potential barriers in the system that could unintentionally exclude people. For example, there are 1 billion people with disabilities around the world. With the advancement of AI, they can access a wide range of information and opportunities more easily in their daily lives. By addressing the barriers, it creates opportunities to innovate and develop AI products with better experiences that benefit everyone. - -> [🎥 Click the here for a video: inclusiveness in AI](https://www.microsoft.com/videoplayer/embed/RE4vl9v) - -### Security and privacy - -AI systems should be safe and respect people’s privacy. People have less trust in systems that put their privacy, information, or lives at risk. When training machine learning models, we rely on data to produce the best results. In doing so, the origin of the data and integrity must be considered. For example, was the data user submitted or publicly available? Next, while working with the data, it is crucial to develop AI systems that can protect confidential information and resist attacks. As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. Privacy and data security issues require especially close attention for AI because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. - -> [🎥 Click the here for a video: security in AI](https://www.microsoft.com/videoplayer/embed/RE4voJF) - -- As an industry we have made significant advancements in Privacy & security, fueled significantly by regulations like the GDPR (General Data Protection Regulation). -- Yet with AI systems we must acknowledge the tension between the need for more personal data to make systems more personal and effective – and privacy. -- Just like with the birth of connected computers with the internet, we are also seeing a huge uptick in the number of security issues related to AI. -- At the same time, we have seen AI being used to improve security. As an example, most modern anti-virus scanners are driven by AI heuristics today. -- We need to ensure that our Data Science processes blend harmoniously with the latest privacy and security practices. - - -### Transparency -AI systems should be understandable. A crucial part of transparency is explaining the behavior of AI systems and their components. Improving the understanding of AI systems requires that stakeholders comprehend how and why they function so that they can identify potential performance issues, safety and privacy concerns, biases, exclusionary practices, or unintended outcomes. We also believe that those who use AI systems should be honest and forthcoming about when, why, and how they choose to deploy them. As well as the limitations of the systems they use. For example, if a bank uses an AI system to support its consumer lending decisions, it is important to examine the outcomes and understand which data influences the system’s recommendations. Governments are starting to regulate AI across industries, so data scientists and organizations must explain if an AI system meets regulatory requirements, especially when there is an undesirable outcome. - -> [🎥 Click the here for a video: transparency in AI](https://www.microsoft.com/videoplayer/embed/RE4voJF) - -- Because AI systems are so complex, it is hard to understand how they work and interpret the results. -- This lack of understanding affects the way these systems are managed, operationalized, and documented. -- This lack of understanding more importantly affects the decisions made using the results these systems produce. - -### Accountability - -The people who design and deploy AI systems must be accountable for how their systems operate. The need for accountability is particularly crucial with sensitive use technologies like facial recognition. Recently, there has been a growing demand for facial recognition technology, especially from law enforcement organizations who see the potential of the technology in uses like finding missing children. However, these technologies could potentially be used by a government to put their citizens’ fundamental freedoms at risk by, for example, enabling continuous surveillance of specific individuals. Hence, data scientists and organizations need to be responsible for how their AI system impacts individuals or society. - -[![Leading AI Researcher Warns of Mass Surveillance Through Facial Recognition](images/accountability.png)](https://www.youtube.com/watch?v=Wldt8P5V6D0 "Microsoft's Approach to Responsible AI") - -> 🎥 Click the image above for a video: Warnings of Mass Surveillance Through Facial Recognition - -Ultimately one of the biggest questions for our generation, as the first generation that is bringing AI to society, is how to ensure that computers will remain accountable to people and how to ensure that the people that design computers remain accountable to everyone else. - -## Impact assessment - -Before training a machine learning model, it is important to conduct an impact assessmet to understand the purpose of the AI system; what the intended use is; where it will be deployed; and who will be interacting with the system. These are helpful for reviewer(s) or testers evaluating the system to know what factors to take into consideration when identifying potential risks and expected consequences. - -The following are areas of focus when conducting an impact assessment: - -* **Adverse impact on individuals**. Being aware of any restriction or requirements, unsupported use or any known limitations hindering the system's performance is vital to ensure that the system is not used in a way that could cause harm to individuals. -* **Data requirements**. Gaining an understanding of how and where the system will use data enables reviewers to explore any data requirements you would need to be mindful of (e.g., GDPR or HIPPA data regulations). In addition, examine whether the source or quantity of data is substantial for training. -* **Summary of impact**. Gather a list of potential harms that could arise from using the system. Throughout the ML lifecycle, review if the issues identified are mitigated or addressed. -* **Applicable goals** for each of the six core principles. Assess if the goals from each of the principles are met and if there are any gaps. - - -## Debugging with responsible AI - -Similar to debugging a software application, debugging an AI system is a necessary process of identifying and resolving issues in the system. There are many factors that would affect a model not performing as expected or responsibly. Most traditional model performance metrics are quantitative aggregates of a model's performance, which are not sufficient to analyze how a model violates the responsible AI principles. Furthermore, a machine learning model is a black box that makes it difficult to understand what drives its outcome or provide explanation when it makes a mistake. Later in this course, we will learn how to use the Responsible AI dashboard to help debug AI systems. The dashboard provides a holistic tool for data scientists and AI developers to perform: - -* **Error analysis**. To identify the error distribution of the model that can affect the system's fairness or reliability. -* **Model overview**. To discover where there are disparities in the model's performance across data cohorts. -* **Data analysis**. To understand the data distribution and identify any potential bias in the data that could lead to fairness, inclusiveness, and reliability issues. -* **Model interpretability**. To understand what affects or influences the model's predictions. This helps in explaining the model's behavior, which is important for transparency and accountability. - - -## 🚀 Challenge - -To prevent harms from being introduced in the first place, we should: - -- have a diversity of backgrounds and perspectives among the people working on systems -- invest in datasets that reflect the diversity of our society -- develop better methods throughout the machine learning lifecycle for detecting and correcting responible AI when it occurs - -Think about real-life scenarios where a model's untrustworthiness is evident in model-building and usage. What else should we consider? - -## [Post-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/6/) -## Review & Self Study - -In this lesson, you have learned some basics of the concepts of fairness and unfairness in machine learning. - -Watch this workshop to dive deeper into the topics: - -- In pursuit of responsible AI: Bringing principles to practice by Besmira Nushi, Mehrnoosh Sameki and Amit Sharma - -[![Responsible AI Toolbox: An open-source framework for building responsible AI](https://img.youtube.com/vi/tGgJCrA-MZU/0.jpg)](https://www.youtube.com/watch?v=tGgJCrA-MZU "RAI Toolbox: An open-source framework for building responsible AI") - -> 🎥 Click the image above for a video: RAI Toolbox: An open-source framework for building responsible AI by Besmira Nushi, Mehrnoosh Sameki, and Amit Sharma - -Also, read: - -- Microsoft’s RAI resource center: [Responsible AI Resources – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4) - -- Microsoft’s FATE research group: [FATE: Fairness, Accountability, Transparency, and Ethics in AI - Microsoft Research](https://www.microsoft.com/research/theme/fate/) - -RAI Toolbox: - -- [Responsible AI Toolbox GitHub repository](https://github.com/microsoft/responsible-ai-toolbox) - -Read about Azure Machine Learning's tools to ensure fairness: - -- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-77952-leestott) - -## Assignment - -[Explore RAI Toolbox](assignment.md) diff --git a/1-Introduction/3-fairness/assignment.md b/1-Introduction/3-fairness/assignment.md deleted file mode 100644 index 6da0a381..00000000 --- a/1-Introduction/3-fairness/assignment.md +++ /dev/null @@ -1,11 +0,0 @@ -# Explore the Responsible AI Toolbox - -## Instructions - -In this lesson you learned about the Responsible AI Toolbox, an "open-source, community-driven project to help data scientists to analyze and improve AI systems." 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