Merge pull request #106 from jlooper/main
adding video link, edits to quizzes
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Jen Looper
GitHub
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d7afc24d03
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@ -12,13 +12,10 @@
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# Artificial Intelligence for Beginners - A Curriculum
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> **This curriculum is being actively developed on GitHub. Look into [contributing](/etc/CONTRIBUTING.md) to see which areas require active contributions. Please consider this a pre-release, and do not actively use in the classroom yet!**
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| ](./lessons/sketchnotes/ai-overview.png)|
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|:---:|
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| AI For Beginners - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ |
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Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about **Artificial Intelligence**.
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In this curriculum, you will learn:
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@ -139,7 +136,7 @@ However, if you would like to take the course as a self-study project, we sugges
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## Meet the Team
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[](https://youtu.be/Tj1XWrDSYJU "Promo video")
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[](https://youtu.be/m2KrAk0cC1c "Promo video")
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> 🎥 Click the image above for a video about the project and the folks who created it!
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@ -22,7 +22,7 @@
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]
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},
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{
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"questionText": "With object detection, we don't just get class of an object, but also it's ____",
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"questionText": "With object detection, we don't just get the class of an object, but also its ____",
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"answerOptions": [
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{
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"answerText": "shape",
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@ -39,7 +39,7 @@
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]
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},
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{
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"questionText": "How many objects can an object detection model can detect?",
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"questionText": "How many objects can an object detection model detect?",
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"answerOptions": [
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{
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"answerText": "one",
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@ -62,7 +62,7 @@
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"title": "Object Detection: Post Quiz",
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"quiz": [
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{
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"questionText": "Object detection model give us",
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"questionText": "An Object detection model gives us",
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"answerOptions": [
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{
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"answerText": "object class",
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@ -9,7 +9,7 @@
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"title": "Segmentation: Pre Quiz",
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"quiz": [
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{
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"questionText": "There are ____ segmentation algorithms?",
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"questionText": "How many segmentation algorithm are there?",
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"answerOptions": [
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{
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"answerText": "1",
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@ -66,7 +66,7 @@
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"title": "Segmentation: Post Quiz",
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"quiz": [
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{
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"questionText": "____ extracts features from input image",
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"questionText": "____ extracts features from an input image",
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"answerOptions": [
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{
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"answerText": "decoder",
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@ -9,7 +9,7 @@
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"title": "Embeddings: Pre Quiz",
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"quiz": [
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{
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"questionText": "Embedding is to represent words with _____ dimensional dense vectors",
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"questionText": "Embedding is used to represent words with _____ dimensional dense vectors",
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"answerOptions": [
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{
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"answerText": "lower",
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@ -83,7 +83,7 @@
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]
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},
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{
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"questionText": "When we train CBoW model, we obtain",
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"questionText": "When we train a CBoW model, we obtain",
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"answerOptions": [
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{
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"answerText": "A model that can generate text",
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@ -100,7 +100,7 @@
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]
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},
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{
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"questionText": "CBoW model is based on",
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"questionText": "The CBoW model is based on",
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"answerOptions": [
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{
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"answerText": "Dense neural network",
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@ -9,7 +9,7 @@
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"title": "Language Models: Pre Quiz",
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"quiz": [
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{
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"questionText": "What is GPT stands for?",
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"questionText": "What does GPT stand for?",
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"answerOptions": [
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{
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"answerText": "Generic Pre-Trained network",
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@ -96,7 +96,7 @@
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]
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},
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{
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"questionText": "Which metric can be used to estimate quality of a language model?",
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"questionText": "Which metric can be used to estimate the quality of a language model?",
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"answerOptions": [
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{
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"answerText": "accuracy",
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@ -262,17 +262,17 @@ Lesson 11B Object Detection: Pre Quiz
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* Neural networks can only be used to classify images
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- true
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+ false
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* With object detection, we don't just get class of an object, but also it's ____
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* With object detection, we don't just get the class of an object, but also its ____
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- shape
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+ location
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- type
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* How many objects can an object detection model can detect?
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* How many objects can an object detection model detect?
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- one
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- two
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+ any number
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Lesson 11E Object Detection: Post Quiz
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* Object detection model give us
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* An Object detection model gives us
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- object class
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- bounding box
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+ both class and bounding box
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@ -286,7 +286,7 @@ Lesson 11E Object Detection: Post Quiz
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+ IoU
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Lesson 12B Segmentation: Pre Quiz
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* There are ____ segmentation algorithms?
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* How many segmentation algorithm are there?
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- 1
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+ 2
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- 3
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@ -300,7 +300,7 @@ Lesson 12B Segmentation: Pre Quiz
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- generator, discriminator
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Lesson 12E Segmentation: Post Quiz
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* ____ extracts features from input image
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* ____ extracts features from an input image
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- decoder
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- generator
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+ encoder
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@ -340,7 +340,7 @@ Lesson 13E Text Representation: Post Quiz
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- false
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Lesson 14B Embeddings: Pre Quiz
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* Embedding is to represent words with _____ dimensional dense vectors
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* Embedding is used to represent words with _____ dimensional dense vectors
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+ lower
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- higher
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- average
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@ -383,11 +383,11 @@ Lesson 15E Language Modeling: Post Quiz
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+ CBoW
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- Skip-gram
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- N-Gram
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* When we train CBoW model, we obtain
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* When we train a CBoW model, we obtain
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- A model that can generate text
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+ Word2Vec embedding vectors
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- Text classification model
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* CBoW model is based on
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* The CBoW model is based on
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+ Dense neural network
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- Convolutional neural network
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- Recurrent neural network
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@ -500,7 +500,7 @@ Lesson 19E Named Entity Recognition: Post Quiz
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+ many-to-many
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Lesson 20B Language Models: Pre Quiz
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* What is GPT stands for?
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* What does GPT stand for?
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- Generic Pre-Trained network
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+ Generative Pre-trained Transformers
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- Generic Positional Text
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@ -521,7 +521,7 @@ Lesson 20E Language Models: Post Quiz
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- Zero-shot learning
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- Few-shot learning
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+ Both
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* Which metric can be used to estimate quality of a language model?
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* Which metric can be used to estimate the quality of a language model?
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- accuracy
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- recall
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+ perplexity
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