regenerating quizzes and adding video link

README.md

@@ -12,13 +12,10 @@
 
 # Artificial Intelligence for Beginners - A Curriculum
 
-> **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!**
-
 |![ Sketchnote by [(@girlie_mac)](https://twitter.com/girlie_mac) ](./lessons/sketchnotes/ai-overview.png)|
 |:---:|
 | AI For Beginners - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ |
 
-
 Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about **Artificial Intelligence**. 
 
 In this curriculum, you will learn:
@@ -139,7 +136,7 @@ However, if you would like to take the course as a self-study project, we sugges
 
 ## Meet the Team
 
-[![Promo video](/lessons/sketchnotes/ai-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
+[![Promo video](/lessons/sketchnotes/ai-for-beginners.png)](https://youtu.be/m2KrAk0cC1c "Promo video")
 
 > 🎥 Click the image above for a video about the project and the folks who created it!
 

etc/quiz-app/src/assets/translations/en/lesson-11.json

@@ -22,7 +22,7 @@
             ]
           },
           {
-            "questionText": "With object detection, we don't just get class of an object, but also it's ____",
+            "questionText": "With object detection, we don't just get the class of an object, but also its ____",
             "answerOptions": [
               {
                 "answerText": "shape",
@@ -39,7 +39,7 @@
             ]
           },
           {
-            "questionText": "How many objects can an object detection model can detect?",
+            "questionText": "How many objects can an object detection model detect?",
             "answerOptions": [
               {
                 "answerText": "one",
@@ -62,7 +62,7 @@
         "title": "Object Detection: Post Quiz",
         "quiz": [
           {
-            "questionText": "Object detection model give us",
+            "questionText": "An Object detection model gives us",
             "answerOptions": [
               {
                 "answerText": "object class",

etc/quiz-app/src/assets/translations/en/lesson-12.json

@@ -9,7 +9,7 @@
         "title": "Segmentation: Pre Quiz",
         "quiz": [
           {
-            "questionText": "There are ____ segmentation algorithms?",
+            "questionText": "How many segmentation algorithm are there?",
             "answerOptions": [
               {
                 "answerText": "1",
@@ -66,7 +66,7 @@
         "title": "Segmentation: Post Quiz",
         "quiz": [
           {
-            "questionText": "____ extracts features from input image",
+            "questionText": "____ extracts features from an input image",
             "answerOptions": [
               {
                 "answerText": "decoder",

etc/quiz-app/src/assets/translations/en/lesson-14.json

@@ -9,7 +9,7 @@
         "title": "Embeddings: Pre Quiz",
         "quiz": [
           {
-            "questionText": "Embedding is to represent words with _____ dimensional dense vectors",
+            "questionText": "Embedding is used to represent words with _____ dimensional dense vectors",
             "answerOptions": [
               {
                 "answerText": "lower",

etc/quiz-app/src/assets/translations/en/lesson-15.json

@@ -83,7 +83,7 @@
             ]
           },
           {
-            "questionText": "When we train CBoW model, we obtain",
+            "questionText": "When we train a CBoW model, we obtain",
             "answerOptions": [
               {
                 "answerText": "A model that can generate text",
@@ -100,7 +100,7 @@
             ]
           },
           {
-            "questionText": "CBoW model is based on",
+            "questionText": "The CBoW model is based on",
             "answerOptions": [
               {
                 "answerText": "Dense neural network",

etc/quiz-app/src/assets/translations/en/lesson-20.json

@@ -9,7 +9,7 @@
         "title": "Language Models: Pre Quiz",
         "quiz": [
           {
-            "questionText": "What is GPT stands for?",
+            "questionText": "What does GPT stand for?",
             "answerOptions": [
               {
                 "answerText": "Generic Pre-Trained network",
@@ -96,7 +96,7 @@
             ]
           },
           {
-            "questionText": "Which metric can be used to estimate quality of a language model?",
+            "questionText": "Which metric can be used to estimate the quality of a language model?",
             "answerOptions": [
               {
                 "answerText": "accuracy",

etc/quiz-src/questions-en.txt

@@ -262,17 +262,17 @@ Lesson 11B Object Detection: Pre Quiz
 * Neural networks can only be used to classify images
 - true
 + false
-* With object detection, we don't just get class of an object, but also it's ____
+* With object detection, we don't just get the class of an object, but also its ____
 - shape
 + location
 - type
-* How many objects can an object detection model can detect?
+* How many objects can an object detection model detect?
 - one
 - two
 + any number
 
 Lesson 11E Object Detection: Post Quiz
-* Object detection model give us
+* An Object detection model gives us
 - object class
 - bounding box
 + both class and bounding box
@@ -286,7 +286,7 @@ Lesson 11E Object Detection: Post Quiz
 + IoU
 
 Lesson 12B Segmentation: Pre Quiz
-* There are ____ segmentation algorithms?
+* How many segmentation algorithm are there?
 - 1
 + 2
 - 3
@@ -300,7 +300,7 @@ Lesson 12B Segmentation: Pre Quiz
 - generator, discriminator
 
 Lesson 12E Segmentation: Post Quiz
-* ____ extracts features from input image
+* ____ extracts features from an input image
 - decoder
 - generator
 + encoder
@@ -340,7 +340,7 @@ Lesson 13E Text Representation: Post Quiz
 - false
 
 Lesson 14B Embeddings: Pre Quiz
-* Embedding is to represent words with _____ dimensional dense vectors
+* Embedding is used to represent words with _____ dimensional dense vectors
 + lower
 - higher
 - average
@@ -383,11 +383,11 @@ Lesson 15E Language Modeling: Post Quiz
 + CBoW
 - Skip-gram
 - N-Gram
-* When we train CBoW model, we obtain
+* When we train a CBoW model, we obtain
 - A model that can generate text
 + Word2Vec embedding vectors
 - Text classification model
-* CBoW model is based on
+* The CBoW model is based on
 + Dense neural network
 - Convolutional neural network
 - Recurrent neural network
@@ -500,7 +500,7 @@ Lesson 19E Named Entity Recognition: Post Quiz
 + many-to-many
 
 Lesson 20B Language Models: Pre Quiz
-* What is GPT stands for?
+* What does GPT stand for?
 - Generic Pre-Trained network
 + Generative Pre-trained Transformers
 - Generic Positional Text
@@ -521,7 +521,7 @@ Lesson 20E Language Models: Post Quiz
 - Zero-shot learning
 - Few-shot learning
 + Both
-* Which metric can be used to estimate quality of a language model?
+* Which metric can be used to estimate the quality of a language model?
 - accuracy
 - recall
 + perplexity