From fdabc37de0551cc7d677fd2253cc56fe31b74a8a Mon Sep 17 00:00:00 2001 From: Flex Zhong Date: Fri, 1 Oct 2021 19:57:38 +0800 Subject: [PATCH 1/3] Data science! (#366) * DS! * Update README.zh-cn.md --- README.md | 4 ++-- translations/README.zh-cn.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 9caa9d6a..b1edd87b 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ > 🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍 -Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson (plus one!) curriculum all about **Machine Learning**. In this curriculum, you will learn about what is sometimes called **classic machine learning**, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our forthcoming 'Data Science for Beginners' curriculum, as well! +Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson (plus one!) curriculum all about **Machine Learning**. In this curriculum, you will learn about what is sometimes called **classic machine learning**, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our ['Data Science for Beginners' curriculum](https://aka.ms/datascience-beginners), as well! Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'. @@ -121,4 +121,4 @@ Our team produces other curricula! Check out: - [Web Dev for Beginners](https://aka.ms/webdev-beginners) - [IoT for Beginners](https://aka.ms/iot-beginners) -- [Data Science for Beginners](https://aka.ms/datascience-beginners) \ No newline at end of file +- [Data Science for Beginners](https://aka.ms/datascience-beginners) diff --git a/translations/README.zh-cn.md b/translations/README.zh-cn.md index e1735cc1..8d8ba6ea 100644 --- a/translations/README.zh-cn.md +++ b/translations/README.zh-cn.md @@ -12,7 +12,7 @@ > 🌍 环游世界，并通过世界文化来探索机器学习 🌍 -微软 Azure Cloud 的倡导者们很高兴可以提供这套十二周、二十四节课的关于**机器学习**的课程。在这套课程中，你将学习关于**经典机器学习**的内容，主要将使用 Scikit-learn 这一库。关于深度学习的内容将会尽量避免 —— 它会被我们即将推出的 "AI for Beginners (针对初学者的 AI 教程)" 所涵盖。你也可以把这些课和我们即将推出的 "Data Science for Beginners (针对初学者的数据科学教程)" 相结合！ +微软 Azure Cloud 的倡导者们很高兴可以提供这套十二周、二十四节课的关于**机器学习**的课程。在这套课程中，你将学习关于**经典机器学习**的内容，主要将使用 Scikit-learn 这一库。关于深度学习的内容将会尽量避免 —— 它会被我们即将推出的 "AI for Beginners (针对初学者的 AI 教程)" 所涵盖。你也可以把这些课和我们已推出的 [Data Science for Beginners（针对初学者的数据科学教程）](https://aka.ms/datascience-beginners) 相结合！ 通过把这些经典的技术应用在来自世界各地的数据，我们将 “环游世界”。每一节课都包括了课前和课后测验、课程内容的文字讲义说明、示例代码、作业等。通过这种基于项目的教学方法，你将在构建中学习，这样可以把技能学的更牢靠。 From 816bbc05d69ff950aeeced519fea2354bf3c9ff1 Mon Sep 17 00:00:00 2001 From: Flex Zhong Date: Fri, 1 Oct 2021 19:58:19 +0800 Subject: [PATCH 2/3] Update README.zh-cn.md (#365) --- translations/README.zh-cn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/translations/README.zh-cn.md b/translations/README.zh-cn.md index 8d8ba6ea..003f7f08 100644 --- a/translations/README.zh-cn.md +++ b/translations/README.zh-cn.md @@ -14,7 +14,7 @@ 微软 Azure Cloud 的倡导者们很高兴可以提供这套十二周、二十四节课的关于**机器学习**的课程。在这套课程中，你将学习关于**经典机器学习**的内容，主要将使用 Scikit-learn 这一库。关于深度学习的内容将会尽量避免 —— 它会被我们即将推出的 "AI for Beginners (针对初学者的 AI 教程)" 所涵盖。你也可以把这些课和我们已推出的 [Data Science for Beginners（针对初学者的数据科学教程）](https://aka.ms/datascience-beginners) 相结合！ -通过把这些经典的技术应用在来自世界各地的数据，我们将 “环游世界”。每一节课都包括了课前和课后测验、课程内容的文字讲义说明、示例代码、作业等。通过这种基于项目的教学方法，你将在构建中学习，这样可以把技能学的更牢靠。 +通过把这些经典的技术应用在来自世界各地的数据，我们将 “环游世界”。每一节课都包括了课前和课后测验、课程内容的文字讲义说明、示例代码、作业等。通过这种基于项目的教学方法，你将在构建中学习，这样可以把技能学得更牢靠。 **✍️ 衷心感谢作者们** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Ornella Altunyan 以及 Amy Boyd @@ -25,7 +25,7 @@ --- # 准备开始 -**对于学生们**，为了更好的使用这套课程，把整个仓库 fork 到你自己的 Github 账户中，并自行（或和一个小组一起）完成以下练习： +**对于学生们**，为了更好的使用这套课程，把整个仓库 fork 到你自己的 GitHub 账户中，并自行（或和一个小组一起）完成以下练习： - 从课前测验开始 - 阅读课程内容，完成所有的活动，在每次 knowledge check 时暂停并思考 @@ -104,7 +104,7 @@ 您可以使用 [Docsify](https://docsify.js.org/#/) 离线运行此文档。 Fork 这个仓库，并在你的本地机器上[安装 Docsify](https://docsify.js.org/#/quickstart)，并在这个仓库的根文件夹中运行 `docsify serve`。你可以通过 localhost 的 3000 端口访问此文档：`localhost:3000`。 ## PDF 文档们 -点击[这里](../pdf/readme.pdf)查找课程的 PDF 文档们。 +点击[这里](../pdf/readme.pdf)查找课程的 PDF 文档。 ## 需要你的帮助！ @@ -116,4 +116,4 @@ - [针对初学者的 Web 开发课程](https://aka.ms/webdev-beginners) - [针对初学者的物联网课程](https://aka.ms/iot-beginners) - +- [针对初学者的数据科学课程](https://aka.ms/datascience-beginners) From 835874614b788a118cb7929c9646ff1f25a5586a Mon Sep 17 00:00:00 2001 From: garanews Date: Fri, 1 Oct 2021 15:18:56 +0200 Subject: [PATCH 3/3] fix typos (#367) --- 2-Regression/2-Data/README.md | 2 +- 2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb | 2 +- 2-Regression/4-Logistic/solution/R/lesson_4.Rmd | 2 +- 4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb | 4 ++-- 4-Classification/1-Introduction/solution/R/lesson_10.Rmd | 4 ++-- 4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb | 2 +- 4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd | 2 +- 5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb | 2 +- 5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb | 4 ++-- 5-Clustering/2-K-Means/solution/R/lesson_15.Rmd | 2 +- 6-NLP/4-Hotel-Reviews-1/README.md | 2 +- 7-TimeSeries/1-Introduction/solution/common/utils.py | 2 +- 7-TimeSeries/1-Introduction/working/common/utils.py | 2 +- 7-TimeSeries/2-ARIMA/solution/common/utils.py | 2 +- 7-TimeSeries/2-ARIMA/working/common/utils.py | 2 +- 8-Reinforcement/1-QLearning/notebook.ipynb | 2 +- 8-Reinforcement/1-QLearning/solution/notebook.ipynb | 2 +- 17 files changed, 20 insertions(+), 20 deletions(-) diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md index ba188810..18faac8e 100644 --- a/2-Regression/2-Data/README.md +++ b/2-Regression/2-Data/README.md @@ -145,7 +145,7 @@ Part of the data scientist's role is to demonstrate the quality and nature of th Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise. -One data visualization libary that works well in Jupyter notebooks is [Matplotlib](https://matplotlib.org/) (which you also saw in the previous lesson). +One data visualization library that works well in Jupyter notebooks is [Matplotlib](https://matplotlib.org/) (which you also saw in the previous lesson). > Get more experience with data visualization in [these tutorials](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa). diff --git a/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb index 80be925d..25320f05 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb +++ b/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb @@ -79,7 +79,7 @@ "\n", "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"ggbeeswarm\"))`\n", "\n", - "Alternatiely, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing." + "Alternately, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing." ], "metadata": { "id": "KPmut75XkmXY" diff --git a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd index a3d332e7..0eeadaf3 100644 --- a/2-Regression/4-Logistic/solution/R/lesson_4.Rmd +++ b/2-Regression/4-Logistic/solution/R/lesson_4.Rmd @@ -48,7 +48,7 @@ You can have them installed as: `install.packages(c("tidyverse", "tidymodels", "janitor", "ggbeeswarm"))` -Alternatiely, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing. +Alternately, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing. ```{r, message=F, warning=F} suppressWarnings(if (!require("pacman"))install.packages("pacman")) diff --git a/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb index 4592429f..a0e93536 100644 --- a/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb +++ b/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb @@ -93,7 +93,7 @@ "\n", "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n", "\n", - "Alternatiely, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing." + "Alternately, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing." ], "metadata": { "id": "ri5bQxZ-Fz_0" @@ -247,7 +247,7 @@ " filter(cuisine == \"korean\")\r\n", "\r\n", "\r\n", - "# Find out how much data is avilable per cuisine\r\n", + "# Find out how much data is available per cuisine\r\n", "cat(\" thai df:\", dim(thai_df), \"\\n\",\r\n", " \"japanese df:\", dim(japanese_df), \"\\n\",\r\n", " \"chinese_df:\", dim(chinese_df), \"\\n\",\r\n", diff --git a/4-Classification/1-Introduction/solution/R/lesson_10.Rmd b/4-Classification/1-Introduction/solution/R/lesson_10.Rmd index b979d56f..f31a7e0b 100644 --- a/4-Classification/1-Introduction/solution/R/lesson_10.Rmd +++ b/4-Classification/1-Introduction/solution/R/lesson_10.Rmd @@ -64,7 +64,7 @@ You can have them installed as: `install.packages(c("tidyverse", "tidymodels", "DataExplorer", "here"))` -Alternatiely, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing. +Alternately, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing. ```{r, message=F, warning=F} suppressWarnings(if (!require("pacman"))install.packages("pacman")) @@ -145,7 +145,7 @@ korean_df <- df %>% filter(cuisine == "korean") -# Find out how much data is avilable per cuisine +# Find out how much data is available per cuisine cat(" thai df:", dim(thai_df), "\n", "japanese df:", dim(japanese_df), "\n", "chinese_df:", dim(chinese_df), "\n", diff --git a/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb index bd1aa914..2d88245a 100644 --- a/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb +++ b/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb @@ -573,7 +573,7 @@ "source": [ "Now we have to decide which algorithm to use for the job 🤔.\r\n", "\r\n", - "In Tidymodels, the [`parsnip package`](https://parsnip.tidymodels.org/index.html) provides consistent interface for working with models across different engines (packages). Please see the parsnip documentation to explore [model types & engines](https://www.tidymodels.org/find/parsnip/#models) and their corresponding [model arguements](https://www.tidymodels.org/find/parsnip/#model-args). The variety is quite bewildering at first sight. For instance, the following methods all include classification techniques:\r\n", + "In Tidymodels, the [`parsnip package`](https://parsnip.tidymodels.org/index.html) provides consistent interface for working with models across different engines (packages). Please see the parsnip documentation to explore [model types & engines](https://www.tidymodels.org/find/parsnip/#models) and their corresponding [model arguments](https://www.tidymodels.org/find/parsnip/#model-args). The variety is quite bewildering at first sight. For instance, the following methods all include classification techniques:\r\n", "\r\n", "- C5.0 Rule-Based Classification Models\r\n", "\r\n", diff --git a/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd b/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd index a4221217..c59d5c90 100644 --- a/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd +++ b/4-Classification/2-Classifiers-1/solution/R/lesson_11.Rmd @@ -158,7 +158,7 @@ Now we are ready to train a model 👩‍💻👨‍💻! Now we have to decide which algorithm to use for the job 🤔. -In Tidymodels, the [`parsnip package`](https://parsnip.tidymodels.org/index.html) provides consistent interface for working with models across different engines (packages). Please see the parsnip documentation to explore [model types & engines](https://www.tidymodels.org/find/parsnip/#models) and their corresponding [model arguements](https://www.tidymodels.org/find/parsnip/#model-args). The variety is quite bewildering at first sight. For instance, the following methods all include classification techniques: +In Tidymodels, the [`parsnip package`](https://parsnip.tidymodels.org/index.html) provides consistent interface for working with models across different engines (packages). Please see the parsnip documentation to explore [model types & engines](https://www.tidymodels.org/find/parsnip/#models) and their corresponding [model arguments](https://www.tidymodels.org/find/parsnip/#model-args). The variety is quite bewildering at first sight. For instance, the following methods all include classification techniques: - C5.0 Rule-Based Classification Models diff --git a/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb b/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb index d05e800f..66f4403b 100644 --- a/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb +++ b/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb @@ -472,7 +472,7 @@ "anaconda-cloud": "", "kernelspec": { "display_name": "R", - "langauge": "R", + "language": "R", "name": "ir" }, "language_info": { diff --git a/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb b/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb index f0dea8ce..7a6d69d9 100644 --- a/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb +++ b/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb @@ -5,7 +5,7 @@ "anaconda-cloud": "", "kernelspec": { "display_name": "R", - "langauge": "R", + "language": "R", "name": "ir" }, "language_info": { @@ -467,7 +467,7 @@ "id": "pLYyt5XSLXzG" }, "source": [ - "The plot shows a large reduction in WCSS (so greater *tightness*) as the number of clusters increases from one to two, and a further noticable reduction from two to three clusters. After that, the reduction is less pronounced, resulting in an `elbow` 💪in the chart at around three clusters. This is a good indication that there are two to three reasonably well separated clusters of data points.\n", + "The plot shows a large reduction in WCSS (so greater *tightness*) as the number of clusters increases from one to two, and a further noticeable reduction from two to three clusters. After that, the reduction is less pronounced, resulting in an `elbow` 💪in the chart at around three clusters. This is a good indication that there are two to three reasonably well separated clusters of data points.\n", "\n", "We can now go ahead and extract the clustering model where `k = 3`:\n", "\n", diff --git a/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd b/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd index 1198f590..2822be2a 100644 --- a/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd +++ b/5-Clustering/2-K-Means/solution/R/lesson_15.Rmd @@ -273,7 +273,7 @@ kclusts %>% geom_point(size = 2, color = "#FF7F0EFF") ``` -The plot shows a large reduction in WCSS (so greater *tightness*) as the number of clusters increases from one to two, and a further noticable reduction from two to three clusters. After that, the reduction is less pronounced, resulting in an `elbow` 💪in the chart at around three clusters. This is a good indication that there are two to three reasonably well separated clusters of data points. +The plot shows a large reduction in WCSS (so greater *tightness*) as the number of clusters increases from one to two, and a further noticeable reduction from two to three clusters. After that, the reduction is less pronounced, resulting in an `elbow` 💪in the chart at around three clusters. This is a good indication that there are two to three reasonably well separated clusters of data points. We can now go ahead and extract the clustering model where `k = 3`: diff --git a/6-NLP/4-Hotel-Reviews-1/README.md b/6-NLP/4-Hotel-Reviews-1/README.md index dd6a573a..60af2b69 100644 --- a/6-NLP/4-Hotel-Reviews-1/README.md +++ b/6-NLP/4-Hotel-Reviews-1/README.md @@ -90,7 +90,7 @@ Here they are grouped in a way that might be easier to examine: | Average Score | Total Number Reviews | Reviewer Score | Negative
Review | Positive Review | Tags | | -------------- | ---------------------- | ---------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- | ----------------------------------------------------------------------------------------- | -| 7.8 | 1945 | 2.5 | This is currently not a hotel but a construction site I was terroized from early morning and all day with unacceptable building noise while resting after a long trip and working in the room People were working all day i e with jackhammers in the adjacent rooms I asked for a room change but no silent room was available To make thinks worse I was overcharged I checked out in the evening since I had to leave very early flight and received an appropiate bill A day later the hotel made another charge without my concent in excess of booked price It s a terrible place Don t punish yourself by booking here | Nothing Terrible place Stay away | Business trip Couple Standard Double Room Stayed 2 nights | +| 7.8 | 1945 | 2.5 | This is currently not a hotel but a construction site I was terroized from early morning and all day with unacceptable building noise while resting after a long trip and working in the room People were working all day i e with jackhammers in the adjacent rooms I asked for a room change but no silent room was available To make thinks worse I was overcharged I checked out in the evening since I had to leave very early flight and received an appropriate bill A day later the hotel made another charge without my concent in excess of booked price It s a terrible place Don t punish yourself by booking here | Nothing Terrible place Stay away | Business trip Couple Standard Double Room Stayed 2 nights | As you can see, this guest did not have a happy stay at this hotel. The hotel has a good average score of 7.8 and 1945 reviews, but this reviewer gave it 2.5 and wrote 115 words about how negative their stay was. If they wrote nothing at all in the Positive_Review column, you might surmise there was nothing positive, but alas they wrote 7 words of warning. If we just counted words instead of the meaning, or sentiment of the words, we might have a skewed view of the reviewers intent. Strangely, their score of 2.5 is confusing, because if that hotel stay was so bad, why give it any points at all? Investigating the dataset closely, you'll see that the lowest possible score is 2.5, not 0. The highest possible score is 10. diff --git a/7-TimeSeries/1-Introduction/solution/common/utils.py b/7-TimeSeries/1-Introduction/solution/common/utils.py index 43bff23f..62684f96 100644 --- a/7-TimeSeries/1-Introduction/solution/common/utils.py +++ b/7-TimeSeries/1-Introduction/solution/common/utils.py @@ -50,7 +50,7 @@ class TimeSeriesTensor(UserDict): - **dataset**: original time series - **target** name of the target column - **H**: the forecast horizon - - **tensor_structures**: a dictionary discribing the tensor structure of the form + - **tensor_structures**: a dictionary describing the tensor structure of the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) } if features are non-sequential and should not be shifted, use the form { 'tensor_name' : (None, [feature, feature, ...])} diff --git a/7-TimeSeries/1-Introduction/working/common/utils.py b/7-TimeSeries/1-Introduction/working/common/utils.py index 43bff23f..62684f96 100644 --- a/7-TimeSeries/1-Introduction/working/common/utils.py +++ b/7-TimeSeries/1-Introduction/working/common/utils.py @@ -50,7 +50,7 @@ class TimeSeriesTensor(UserDict): - **dataset**: original time series - **target** name of the target column - **H**: the forecast horizon - - **tensor_structures**: a dictionary discribing the tensor structure of the form + - **tensor_structures**: a dictionary describing the tensor structure of the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) } if features are non-sequential and should not be shifted, use the form { 'tensor_name' : (None, [feature, feature, ...])} diff --git a/7-TimeSeries/2-ARIMA/solution/common/utils.py b/7-TimeSeries/2-ARIMA/solution/common/utils.py index 43bff23f..62684f96 100644 --- a/7-TimeSeries/2-ARIMA/solution/common/utils.py +++ b/7-TimeSeries/2-ARIMA/solution/common/utils.py @@ -50,7 +50,7 @@ class TimeSeriesTensor(UserDict): - **dataset**: original time series - **target** name of the target column - **H**: the forecast horizon - - **tensor_structures**: a dictionary discribing the tensor structure of the form + - **tensor_structures**: a dictionary describing the tensor structure of the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) } if features are non-sequential and should not be shifted, use the form { 'tensor_name' : (None, [feature, feature, ...])} diff --git a/7-TimeSeries/2-ARIMA/working/common/utils.py b/7-TimeSeries/2-ARIMA/working/common/utils.py index 43bff23f..62684f96 100644 --- a/7-TimeSeries/2-ARIMA/working/common/utils.py +++ b/7-TimeSeries/2-ARIMA/working/common/utils.py @@ -50,7 +50,7 @@ class TimeSeriesTensor(UserDict): - **dataset**: original time series - **target** name of the target column - **H**: the forecast horizon - - **tensor_structures**: a dictionary discribing the tensor structure of the form + - **tensor_structures**: a dictionary describing the tensor structure of the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) } if features are non-sequential and should not be shifted, use the form { 'tensor_name' : (None, [feature, feature, ...])} diff --git a/8-Reinforcement/1-QLearning/notebook.ipynb b/8-Reinforcement/1-QLearning/notebook.ipynb index c238ab34..d3c63846 100644 --- a/8-Reinforcement/1-QLearning/notebook.ipynb +++ b/8-Reinforcement/1-QLearning/notebook.ipynb @@ -153,7 +153,7 @@ "source": [ "## Q-Learning\n", "\n", - "Build a Q-Table, or multi-dimensional array. Since our board has dimentions `width` x `height`, we can represent Q-Table by a numpy array with shape `width` x `height` x `len(actions)`:" + "Build a Q-Table, or multi-dimensional array. Since our board has dimensions `width` x `height`, we can represent Q-Table by a numpy array with shape `width` x `height` x `len(actions)`:" ], "cell_type": "markdown", "metadata": {} diff --git a/8-Reinforcement/1-QLearning/solution/notebook.ipynb b/8-Reinforcement/1-QLearning/solution/notebook.ipynb index 1bf4433b..de19ad0b 100644 --- a/8-Reinforcement/1-QLearning/solution/notebook.ipynb +++ b/8-Reinforcement/1-QLearning/solution/notebook.ipynb @@ -253,7 +253,7 @@ "source": [ "## Q-Learning\n", "\n", - "Build a Q-Table, or multi-dimensional array. Since our board has dimentions `width` x `height`, we can represent Q-Table by a numpy array with shape `width` x `height` x `len(actions)`:" + "Build a Q-Table, or multi-dimensional array. Since our board has dimensions `width` x `height`, we can represent Q-Table by a numpy array with shape `width` x `height` x `len(actions)`:" ], "cell_type": "markdown", "metadata": {}