Merge pull request #104 from CinnamonXI/main

copyedit

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

@@ -39,7 +39,7 @@
             ]
           },
           {
-            "questionText": "How many objects an object detection model can detect?",
+            "questionText": "How many objects can an object detection model can detect?",
             "answerOptions": [
               {
                 "answerText": "one",

etc/quiz-app/src/assets/translations/en/lesson-18.json

@@ -9,7 +9,7 @@
         "title": "Transformers: Pre Quiz",
         "quiz": [
           {
-            "questionText": "Attention mechanism provides a means of _____ the imoact of an inout vector on an output prediction of RNN",
+            "questionText": "Attention mechanism provides a means of _____ the impact of an inout vector on an output prediction of RNN",
             "answerOptions": [
               {
                 "answerText": "weighting",

etc/quiz-app/src/assets/translations/en/lesson-19.json

@@ -9,7 +9,7 @@
         "title": "Named Entity Recognition: Pre Quiz",
         "quiz": [
           {
-            "questionText": "What is NER stands for?",
+            "questionText": "What does NER stands for?",
             "answerOptions": [
               {
                 "answerText": "Nearest Estimated Region",
@@ -70,7 +70,7 @@
               },
               {
                 "answerText": "token classification",
-                "isCorrect": false
+                "isCorrect": true
               },
               {
                 "answerText": "text regression",

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

@@ -91,7 +91,7 @@
               },
               {
                 "answerText": "Both",
-                "isCorrect": false
+                "isCorrect": true
               }
             ]
           },

etc/quiz-app/src/assets/translations/en/lesson-22.json

@@ -83,7 +83,7 @@
             ]
           },
           {
-            "questionText": "Which problems RL is applicable to?",
+            "questionText": "Which problem(s) is RL is applicable to?",
             "answerOptions": [
               {
                 "answerText": "With discrete environment",

etc/quiz-src/questions-en.txt

@@ -266,7 +266,7 @@ Lesson 11B Object Detection: Pre Quiz
 - shape
 + location
 - type
-* How many objects an object detection model can detect?
+* How many objects can an object detection model can detect?
 - one
 - two
 + any number
@@ -446,7 +446,7 @@ Lesson 17E Generative networks: Post Quiz
 - one-to-many
 
 Lesson 18B Transformers: Pre Quiz
-* Attention mechanism provides a means of _____ the imoact of an inout vector on an output prediction of RNN
+* Attention mechanism provides a means of _____ the impact of an inout vector on an output prediction of RNN
 + weighting
 - training
 - testing
@@ -473,7 +473,7 @@ Lesson 18E Transformers: Post Quiz
 - 3
 
 Lesson 19B Named Entity Recognition: Pre Quiz
-* What is NER stands for?
+* What does NER stands for?
 - Nearest Estimated Region
 - Nearest Entity Region
 + Named Entity Recognition
@@ -488,7 +488,7 @@ Lesson 19B Named Entity Recognition: Pre Quiz
 Lesson 19E Named Entity Recognition: Post Quiz
 * NER model is essentially a ____ model
 - text classification
-- token classification
++ token classification
 - text regression
 * Which neural network types can be used for NER?
 - RNNs
@@ -520,7 +520,7 @@ Lesson 20E Language Models: Post Quiz
 * Prompt engineering can be used with
 - Zero-shot learning
 - Few-shot learning
-- Both
++ Both
 * Which metric can be used to estimate quality of a language model?
 - accuracy
 - recall
@@ -571,7 +571,7 @@ Lesson 22E Reinforcement Learning: Post Quiz
 - It achieves high accuracy
 - Using perplexity metric
 + Using reward function
-* Which problems RL is applicable to?
+* Which problem(s) is RL is applicable to?
 - With discrete environment
 - With continuous environment
 + Both

lessons/4-ComputerVision/11-ObjectDetection/README.md

@@ -11,8 +11,9 @@ The image classification models we have dealt with so far took an image and prod
 ## A Naive Approach to Object Detection
 
 Assuming we wanted to find a cat on a picture, a very naive approach to object detection would be the following:
-1.  Break the picture down to a number of tiles
-2. Run image classification on each tile. 
+
+1. Break the picture down to a number of tiles
+2. Run image classification on each tile.
 3. Those tiles that result in sufficiently high activation can be considered to contain the object in question.
 
 ![Naive Object Detection](images/naive-detection.png)
@@ -50,8 +51,8 @@ The idea is simple - we divide the area of intersection between two figures by t
 
 Suppose we want to measure how well a given class of objects $C$ is recognized. To measure it, we use **Average Precision** metrics, which is calculated as follows:
 
-1. Consider Precision-Recall curve shows the accuracy depending on a detection threshold value (from 0 to 1). 
-2. Depending on the threshold, we will get more or less objects detected in the image, and different values of precision and recall. 
+1. Consider Precision-Recall curve shows the accuracy depending on a detection threshold value (from 0 to 1).
+2. Depending on the threshold, we will get more or less objects detected in the image, and different values of precision and recall.
 3. The curve will look like this:
 
 <img src="https://github.com/shwars/NeuroWorkshop/raw/master/images/ObjDetectionPrecisionRecall.png"/>
@@ -118,7 +119,7 @@ This algorithm is even faster than Faster R-CNN. The main idea is the following:
 
 1. We extract features using ResNet-101
 1. Features are processed by **Position-Sensitive Score Map**. Each object from $C$ classes is divided by $k\times k$ regions, and we are training to predict parts of objects.
-1. For each part from $k\times k$ regions all networks vote for object classes, and the object class with maximum vote is selected. 
+1. For each part from $k\times k$ regions all networks vote for object classes, and the object class with maximum vote is selected.
 
 ![r-fcn image](images/r-fcn.png)
 
@@ -127,14 +128,14 @@ This algorithm is even faster than Faster R-CNN. The main idea is the following:
 ### YOLO - You Only Look Once
 
 YOLO is a realtime one-pass algorithm. The main idea is the following:
- 
+
  * Image is divided into $S\times S$ regions
  * For each region, **CNN** predicts $n$ possible objects, *bounding box* coordinates and *confidence*=*probability* * IoU.
- 
+
  ![YOLO](images/yolo.png)
- 
+
 > Image from [official paper](https://arxiv.org/abs/1506.02640)
- 
+
 ### Other Algorithms
 
 * RetinaNet: [official paper](https://arxiv.org/abs/1708.02002)
@@ -161,7 +162,7 @@ Read through these articles and notebooks about YOLO and try them for yourself
  * [Official site](https://pjreddie.com/darknet/yolo/)
  * Yolo: [Keras implementation](https://github.com/experiencor/keras-yolo2), [step-by-step notebook](https://github.com/experiencor/basic-yolo-keras/blob/master/Yolo%20Step-by-Step.ipynb)
  * Yolo v2: [Keras implementation](https://github.com/experiencor/keras-yolo2), [step-by-step notebook](https://github.com/experiencor/keras-yolo2/blob/master/Yolo%20Step-by-Step.ipynb)
- 
+
 ## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/211)
 
 ## Review & Self Study
@@ -173,4 +174,3 @@ Read through these articles and notebooks about YOLO and try them for yourself
 * [Implementation of Faster R-CNN in Python for Object Detection](https://www.analyticsvidhya.com/blog/2018/11/implementation-faster-r-cnn-python-object-detection/)
 
 ## [Assignment: Object Detection](lab/README.md)
-

lessons/5-NLP/15-LanguageModeling/README.md

@@ -38,4 +38,4 @@ In the previous lesson we have seen that words embeddings work like magic! Now w
 
 ## 🚀 [Assignment: Train Skip-Gram Model](lab/README.md)
 
-In the lab, we challenge you to modify the code from this lesson to train skip-gram model instead of CBoW. [Read the details](lab/README.md) 
+In the lab, we challenge you to modify the code from this lesson to train skip-gram model instead of CBoW. [Read the details](lab/README.md)

lessons/5-NLP/19-NER/README.md

@@ -2,7 +2,7 @@
 
 Up to now, we have mostly been concentrating on one NLP task - classification. However, there are also other NLP tasks that can be accomplished with neural networks. One of those tasks is **[Named Entity Recognition](https://wikipedia.org/wiki/Named-entity_recognition)** (NER), which deals with recognizing specific entities within text, such as places, person names, date-time intervals, chemical formulae and so on.
 
-## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/119) 
+## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/119)
 
 ## Example of Using NER
 
@@ -12,7 +12,7 @@ Suppose you want to develop a natural language chat bot, similar to Amazon Alexa
 
 > Image by the author
 
-However, a user may provide some parameters as part of the phrase. For example, when asking for the weather, she may specify a location or date. A bot should be able to understand those entities, and fill in the parameter slots accordingly before performing the action. This is exactly where NER comes in. 
+However, a user may provide some parameters as part of the phrase. For example, when asking for the weather, she may specify a location or date. A bot should be able to understand those entities, and fill in the parameter slots accordingly before performing the action. This is exactly where NER comes in.
 
 > ✅ Another example would be [analyzing scientific medical papers](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/). One of the main things we need to look for are specific medical terms, such as diseases and medical substances. While a small number of diseases can probably be extracted using substring search, more complex entities, such as chemical compounds and medication names, need a more complex approach.
 
@@ -25,11 +25,12 @@ Consider the following paper title:
 **Tricuspid valve regurgitation** and **lithium carbonate** **toxicity** in a newborn infant.
 
 Entities here are:
+
 * Tricuspid valve regurgitation is a disease (`DIS`)
 * Lithium carbonate is a chemical substance (`CHEM`)
 * Toxicity is also a disease (`DIS`)
 
-Notice that one entity can span several tokens. And, as in this case, we need to distinguish between two consecutive entities. Thus it is common to use two classes for each entity - one specifying the first token of the entity (often the `B-` prefix is used, for **b**eginning), and another - the continuation of an entity (`I-`, for **i**nner token). We also use `O` as a class to represent all **o**ther tokens. Such token tagging is called [BIO tagging](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)) (or IOB). When tagged, our title will look like this:
+Notice that one entity can span several tokens. And, as in this case, we need to distinguish between two consecutive entities. Thus, it is common to use two classes for each entity - one specifying the first token of the entity (often the `B-` prefix is used, for **b**eginning), and another - the continuation of an entity (`I-`, for **i**nner token). We also use `O` as a class to represent all **o**ther tokens. Such token tagging is called [BIO tagging](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)) (or IOB). When tagged, our title will look like this:
 
 Token | Tag
 ------|-----
@@ -64,7 +65,7 @@ Continue your learning in the following notebook:
 
 ## Conclusion
 
-A NER model is a **token classification model**, which means that it can be used to perform token classification. This is a very common task in NLP, helping to recognize specific entities within text including places, names, dates, and more. 
+A NER model is a **token classification model**, which means that it can be used to perform token classification. This is a very common task in NLP, helping to recognize specific entities within text including places, names, dates, and more.
 
 ## 🚀 Challenge
 
@@ -79,4 +80,3 @@ Read through the blog [The Unreasonable Effectiveness of Recurrent Neural Networ
 ## [Assignment](lab/README.md)
 
 In the assignment for this lesson, you will have to train a medical entity recognition model. You can start with training an LSTM model as described in this lesson, and proceed with using the BERT transformer model. Read [the instructions](lab/README.md) to get all the details.
-

lessons/5-NLP/20-LangModels/README.md

@@ -6,9 +6,9 @@ In all of our previous tasks, we were training a neural network to perform a cer
 
 ## Text Generation and Perplexity
 
-The idea of a neural network being able to do general tasks without downstream training is presented in [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) paper. The main idea is the many other tasks can be modeled using **text generation**, because understanding text essentially means being able to produce it. Because the model is trained on a huge amount of text that encompasses human knowledge, it also becomes knowledgeable about wide variety of subjects. 
+The idea of a neural network being able to do general tasks without downstream training is presented in [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) paper. The main idea is the many other tasks can be modeled using **text generation**, because understanding text essentially means being able to produce it. Because the model is trained on a huge amount of text that encompasses human knowledge, it also becomes knowledgeable about wide variety of subjects.
 
-> Understanding and being able to produce text also entails knowing something about the world around us. People  also learn by reading to the large extent, and GPT network is similar in this respect. 
+> Understanding and being able to produce text also entails knowing something about the world around us. People  also learn by reading to the large extent, and GPT network is similar in this respect.
 
 Text generation networks work by predicting probability of the next word $P(w_N)$. However, unconditional probability of the next word equals to the frequency of the this word in the text corpus. GPT is able to give us **conditional probability** of the next word, given the previous ones $P(w_N | w_{n-1}, ..., w_0)$.
 
@@ -54,4 +54,3 @@ Continue your learning in the following notebooks:
 New general pre-trained language models do not only model language structure, but also contain vast amount of commonsense knowledge. Thus, they can be effectively used to solve some NLP tasks in zero-shop or few-shot settings.
 
 ## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/220)
-

lessons/6-Other/22-DeepRL/README.md

@@ -7,7 +7,7 @@ Reinforcement learning (RL) is seen as one of the basic machine learning paradig
 To perform RL, we need:
 
 * An **environment** or **simulator** that sets the rules of the game. We should be able to run the experiments in the simulator and observe the results.
-* Some **Reward function**, which indicates how successful our experiment was. In case of learning to play a computer game, the reward would be our final score. 
+* Some **Reward function**, which indicates how successful our experiment was. In case of learning to play a computer game, the reward would be our final score.
 
 Based on the reward function, we should be able to adjust our behavior and improve our skills, so that the next time we play better. The main difference between other types of machine learning and RL is that in RL we typically do not know whether we win or lose until we finish the game. Thus, we cannot say whether a certain move alone is good or not - we only receive a reward at the end of the game.
 
@@ -17,7 +17,7 @@ During RL, we typically perform many experiments. During each experiment, we nee
 
 A great tool for RL is the [OpenAI Gym](https://gym.openai.com/) - a **simulation environment**, which can simulate many different environments starting from Atari games, to the physics behind pole balancing. It is one of the most popular simulation environments for training reinforcement learning algorithms, and is maintained by [OpenAI](https://openai.com/).
 
-> **Note**: You can see all the environments available from OpenAI Gym [here](https://gym.openai.com/envs/#classic_control). 
+> **Note**: You can see all the environments available from OpenAI Gym [here](https://gym.openai.com/envs/#classic_control).
 
 ## CartPole Balancing
 
@@ -110,4 +110,4 @@ Watch [this great video](https://www.youtube.com/watch?v=qv6UVOQ0F44) talking ab
 
 ## Assignment: [Train a Mountain Car](lab/README.md)
 
-Your goal during this assignment would be to train a different Gym environment - [Mountain Car](https://www.gymlibrary.ml/environments/classic_control/mountain_car/).
+Your goal during this assignment would be to train a different Gym environment - [Mountain Car](https://www.gymlibrary.ml/environments/classic_control/mountain_car/).