* Adding content

* Update en.json

* Update README.md

* Update TRANSLATIONS.md

* Adding lesson tempolates

* Fixing code files with each others code in

* Update README.md

* Adding lesson 16

* Adding virtual camera

* Adding Wio Terminal camera capture

* Adding wio terminal code

* Adding SBC classification to lesson 16

* Adding challenge, review and assignment

* Adding images and using new Azure icons

* Update README.md

* Update iot-reference-architecture.png

* Adding structure for JulyOT links

* Removing icons

* Sketchnotes!

* Create lesson-1.png

* Starting on lesson 18

* Updated sketch

* Adding virtual distance sensor

* Adding Wio Terminal image classification

* Update README.md

* Adding structure for project 6 and wio terminal distance sensor

* Adding some of the smart timer stuff

* Updating sketchnotes

* Adding virtual device speech to text

* Adding chapter 21

* Language tweaks

* Lesson 22 stuff

* Update en.json

* Bumping seeed libraries

* Adding functions lab to lesson 22

* Almost done with LUIS

* Update README.md

* Reverting sunlight sensor change

Fixes #88

* Structure

* Adding speech to text lab for Pi

* Adding virtual device text to speech lab

* Finishing lesson 23

* Clarifying privacy

Fixes #99

* Update README.md

* Update hardware.md

* Update README.md

* Fixing some code samples that were wrong

* Adding more on translation

* Adding more on translator

* Update README.md

* Update README.md

* Adding public access to the container

* First part of retail object detection

* More on stock lesson

* Tweaks to maps lesson

* Update README.md

* Update pi-sensor.md

* IoT Edge install stuffs

* Notes on consumer groups and not running the event monitor at the same time

* Assignment for object detector

* Memory notes for speech to text

* Migrating LUIS to an HTTP trigger

* Adding Wio Terminal speech to text

* Changing smart timer to functions from hub

* Changing a param to body to avoid URL encoding

* Update README.md

* Tweaks before IoT Show

* Adding sketchnote links

* Adding object detection labs

* Adding more on object detection

* More on stock detection

* Finishing stock counting

* Tidying stuff

* Adding wio purchase link

* Updating Seeed logo

* Update pi-proximity.md

* Fix clean up link

Fixes #145

* Moving attributions to a separate file

* First draft of edge classifier

* Adding extras

* Moving folder

* Adding lesson 11 questions

* Image improvements

* More image tweaks

* Adding lesson 12 quiz

* Quiz for lesson 13

* Adding quiz for lesson 14

* Lesson 15 and 16 quiz
pull/178/head
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@ -1423,6 +1423,202 @@
] ]
} }
] ]
},
{
"id": 29,
"title": "Lesson 15 - Train a fruit quality detector: Pre-Lecture Quiz",
"quiz": [
{
"questionText": "Cameras can be used as IoT sensors",
"answerOptions": [
{
"answerText": "True",
"isCorrect": "true"
},
{
"answerText": "False",
"isCorrect": "false"
}
]
},
{
"questionText": "Fruit can be sorted using cameras",
"answerOptions": [
{
"answerText": "True",
"isCorrect": "true"
},
{
"answerText": "False",
"isCorrect": "false"
}
]
},
{
"questionText": "Images-based AI models are incredibly complex and time consuming to train, requiring hundreds of thousands of images:",
"answerOptions": [
{
"answerText": "True",
"isCorrect": "false"
},
{
"answerText": "False",
"isCorrect": "true"
}
]
}
]
},
{
"id": 30,
"title": "Lesson 15 - Train a fruit quality detector: Post-Lecture Quiz",
"quiz": [
{
"questionText": "The technique custom vision uses to train a model with only a few images is called:",
"answerOptions": [
{
"answerText": "Transformational learning",
"isCorrect": "false"
},
{
"answerText": "Transaction learning",
"isCorrect": "false"
},
{
"answerText": "Transfer learning",
"isCorrect": "true"
}
]
},
{
"questionText": "Image classifiers can be trained using:",
"answerOptions": [
{
"answerText": "Only 1 image per tag",
"isCorrect": "false"
},
{
"answerText": "At least 5 images per tag",
"isCorrect": "true"
},
{
"answerText": "At least 50 images per tag",
"isCorrect": "false"
}
]
},
{
"questionText": "The hardware that allows ML models to be trained quickly, as well as making the graphics on out Xbox look amazing are called:",
"answerOptions": [
{
"answerText": "PGU",
"isCorrect": "false"
},
{
"answerText": "GPU",
"isCorrect": "true"
},
{
"answerText": "PUG",
"isCorrect": "false"
}
]
}
]
},
{
"id": 31,
"title": "Lesson 16 - Check fruit quality from an IoT device: Pre-Lecture Quiz",
"quiz": [
{
"questionText": "IoT devices are not powerful enough to use cameras:",
"answerOptions": [
{
"answerText": "True",
"isCorrect": "false"
},
{
"answerText": "False",
"isCorrect": "true"
}
]
},
{
"questionText": "Camera sensors use film to capture images",
"answerOptions": [
{
"answerText": "True",
"isCorrect": "false"
},
{
"answerText": "False",
"isCorrect": "true"
}
]
},
{
"questionText": "Camera sensors send which type of data",
"answerOptions": [
{
"answerText": "Digital",
"isCorrect": "true"
},
{
"answerText": "Analog",
"isCorrect": "false"
}
]
}
]
},
{
"id": 32,
"title": "Lesson 16 - Check fruit quality from an IoT device: Post-Lecture Quiz",
"quiz": [
{
"questionText": "A published version of a custom vision model is called an:",
"answerOptions": [
{
"answerText": "Iteration",
"isCorrect": "true"
},
{
"answerText": "Instance",
"isCorrect": "false"
},
{
"answerText": "Iguana",
"isCorrect": "false"
}
]
},
{
"questionText": "When images are sent for classification, they then become available to retrain the model:",
"answerOptions": [
{
"answerText": "True",
"isCorrect": "true"
},
{
"answerText": "False",
"isCorrect": "false"
}
]
},
{
"questionText": "You don't need to use images captured from an IoT device to train the model as the cameras are as good quality as phone cameras:",
"answerOptions": [
{
"answerText": "True",
"isCorrect": "false"
},
{
"answerText": "False",
"isCorrect": "true"
}
]
}
]
} }
] ]
} }

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