Lesson 8 and fixed the broken links

Author: CinnamonXI

etc/quiz-src/questions-en.txt

@@ -1,4 +1,4 @@
-Lesson 1B Introduction to AI - Pre Quiz
+Lesson 1B Introduction to AI: Pre Quiz
 * A famous 19th century proto-computer engineer was
 - Charles Barkley
 + Charles Babbage
@@ -11,7 +11,7 @@ Lesson 1B Introduction to AI - Pre Quiz
 - true, they are usually considered to be 'intelligent
 + false, but they are increasingly able to pass Turing tests as they become more sophisticated.
 
-Lesson 1E Introduction to AI - Post-Quiz
+Lesson 1E Introduction to AI: Post-Quiz
 * A top-down approach to AI is a model of reasoning called
 - strategic reasoning
 + symbolic reasoning
@@ -76,7 +76,7 @@ Lesson 3E Introduction to Neural Networks - Perceptron: Post-Quiz
 + weights
 - gradient
 
-Lesson 4B Neural Networks - Pre Quiz
+Lesson 4B Neural Networks: Pre Quiz
 * The quality of prediction is measured by Loss function 
 + True
 - False
@@ -89,7 +89,7 @@ Lesson 4B Neural Networks - Pre Quiz
 - multiple propagation
 - front propagation
 
-Lesson 4E Neural Networks - Post Quiz
+Lesson 4E Neural Networks: Post Quiz
 * We use ____ for regression loss functions
 - absolute error
 - mean squared error
@@ -105,7 +105,7 @@ Lesson 4E Neural Networks - Post Quiz
 + True
 - False
 
-Lesson 5B Frameworks - Pre Quiz
+Lesson 5B Frameworks: Pre Quiz
 * Deep Neural Network training requires a lot of computations
 + True
 - False
@@ -118,7 +118,7 @@ Lesson 5B Frameworks - Pre Quiz
 + algorithm
 - computer
 
-Lesson 5E Frameworks - Post Quiz
+Lesson 5E Frameworks: Post Quiz
 * After compiling our model object, we train by calling ____ function
 + fit
 - train
@@ -133,3 +133,54 @@ Lesson 5E Frameworks - Post Quiz
 * Pred is the values predicted by the network
 + True
 - False
+
+Lesson 7B Convolutional Neural Networks: Pre Quiz
+* To extract patterns from images we use?
++ convolutional filters
+- extractor
+- filters
+* One of these is not a CNN Architecture
+- ResNet
+- MobileNet
++ Tensorflow
+* CNN are mostly used for computer vision tasks.
++ true
+- false
+
+Lesson 7E Convolutional Neural Networks: Post Quiz
+* Which pooling layer is used "scale down" the size of the image
+- average pooling
+- max pooling
++ a and b
+* Convolutional networks generalizes much better
++ True
+- False
+* To train our neural network, we need to convert images to tensors
++ true
+- false
+
+Lesson 8B Pre-trained Networks and Transfer Learning: Pre Quiz
+* Transfer learning approach uses untrained models for classification
+- true
++ false
+* One of these is not a normalization technique?
++ height normalization
+- weight normalization
+- layer normalization
+* We choose Stochastic Gradient Descent(SGD) in deep learning because classical gradient descent can be ____
+- fast
++ slow
+
+Lesson 8E Pre-trained Networks and Transfer Learning: Post Quiz
+* Dropout layers act as a ____ technique
+- gradient boosting
+- training
++ regularization
+* freezing weights of convolutional feature extractor can be done by ____
+- setting `requires_grad` property to `False`
+- setting `trainable` property to `False`
++ a and b
+* Batch normalization is to bring values that flow through the ____ to right interval
+- algorithms
+- batches
++ neural network

lessons/2-Symbolic/README.md

@@ -6,7 +6,7 @@
 
 The quest for artificial intelligence is based on a search for knowledge, to make sense of the world similar to how humans do. But how can you go about doing this?
 
-## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/201)
+## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/102)
 
 In the early days of AI, the top-down approach to creating intelligent systems (discussed in the previous lesson) was popular. The idea was to extract the knowledge from people into some machine-readable form, and then use it to automatically solve problems. This approach was based on two big ideas:
 

lessons/3-NeuralNetworks/03-Perceptron/README.md

@@ -1,6 +1,6 @@
 # Introduction to Neural Networks: Perceptron
 
-## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/301)
+## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/103)
 
 One of the first attempts to implement something similar to a modern neural network was done by Frank Rosenblatt from Cornell Aeronautical Laboratory in 1957. It was a hardware implementation called "Mark-1", designed to recognize primitive geometric figures, such as triangles, squares and circles.
 
@@ -76,7 +76,7 @@ In this lesson, you learned about a perceptron, which is a binary classification
 
 If you'd like to try to build your own perceptron, try [this lab on Microsoft Learn](https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-averaged-perceptron?WT.mc_id=academic-57639-dmitryso) which uses the [Azure ML designer](https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer?WT.mc_id=academic-57639-dmitryso).
 
-## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/302)
+## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/203)
 
 ## Review & Self Study
 

lessons/3-NeuralNetworks/04-OwnFramework/README.md

@@ -10,7 +10,7 @@ In this section we will extend this model into a more flexible framework, allowi
 
 We will also develop our own modular framework in Python that will allow us to construct different neural network architectures.
 
-## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/401)
+## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/104)
 
 ## Formalization of Machine Learning
 
@@ -64,15 +64,15 @@ Note that the left-most part of all those expressions is the same, and thus we c
 
 ## Conclusion
 
-In this lesson, we have built our own neural network library, and we have used it for a simple two-dimensional classification task. 
+In this lesson, we have built our own neural network library, and we have used it for a simple two-dimensional classification task.
 
 ## 🚀 Challenge
 
-In the accompanying notebook, you will implement your own framework for building and training multi-layered perceptrons. You will be able to see in detail how modern neural networks operate. 
+In the accompanying notebook, you will implement your own framework for building and training multi-layered perceptrons. You will be able to see in detail how modern neural networks operate.
 
 Proceed to the [OwnFramework](OwnFramework.ipynb) notebook and work through it.
 
-## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/402)
+## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/204)
 
 ## Review & Self Study
 
@@ -83,4 +83,4 @@ Backpropagation is a common algorithm used in AI and ML, worth studying [in more
 In this lab, you are asked to use the framework you constructed in this lesson to solve MNIST handwritten digit classification.
 
 * [Instructions](lab/README.md)
-* [Notebook](lab/MyFW_MNIST.ipynb)
+* [Notebook](lab/MyFW_MNIST.ipynb)

lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb

@@ -1026,7 +1026,7 @@
       "source": [
         "## Training One-Layer Perceptron\n",
         "\n",
-        "Let's use Tensorflow gradient computing machinery to train one-layer perceptron.\n",
+        "Let's use PyTorch gradient computing machinery to train one-layer perceptron.\n",
         "\n",
         "Our neural network will have 2 inputs and 1 output. The weight matrix $W$ will have size $2\\times1$, and bias vector $b$ -- $1$.\n",
         "\n",

lessons/3-NeuralNetworks/05-Frameworks/Overfitting.md

@@ -25,7 +25,7 @@ It is very important to strike a correct balance between the richness of the mod
 
 As you can see from the graph above, overfitting can be detected by a very low training error, and a high validation error. Normally during training we will see both training and validation errors starting to decrease, and then at some point validation error might stop decreasing and start rising. This will be a sign of overfitting, and the indicator that we should probably stop training at this point (or at least make a snapshot of the model).
 
-![overfitting]("../images/Overfitting.png")
+![overfitting](../images/Overfitting.png)
 
 ## How to prevent overfitting
 
@@ -38,10 +38,11 @@ If you can see that overfitting occurs, you can do one of the following:
 ## Overfitting and Bias-Variance Tradeoff
 
 Overfitting is actually a case of a more generic problem in statistics called [Bias-Variance Tradeoff](https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff). If we consider the possible sources of error in our model, we can see two types of errors:
+
 * **Bias errors** are caused by our algorithm not being able to capture the relationship between training data correctly. It can result from the fact that our model is not powerful enough (**underfitting**).
 * **Variance errors**, which are caused by the model approximating noise in the input data instead of meaningful relationship (**overfitting**).
 
-During training, bias error decreases (as our model learns to approximate the data), and variance error increases. It is important to stop training - either manually (when we detect overfitting) or automatically (by introducing regularization) - to prevent overfitting. 
+During training, bias error decreases (as our model learns to approximate the data), and variance error increases. It is important to stop training - either manually (when we detect overfitting) or automatically (by introducing regularization) - to prevent overfitting.
 
 ## Conclusion
 
@@ -51,16 +52,18 @@ In this lesson, you learned about the differences between the various APIs for t
 
 In the accompanying notebooks, you will find 'tasks' at the bottom; work through the notebooks and complete the tasks.
 
-## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/502)
+## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/205)
 
 ## Review & Self Study
 
 Do some research on the following topics:
+
 - TensorFlow
 - PyTorch
 - Overfitting
 
-Ask yourself the following questions: 
+Ask yourself the following questions:
+
 - What is the difference between TensorFlow and PyTorch?
 - What is the difference between overfitting and underfitting?
 

lessons/3-NeuralNetworks/05-Frameworks/README.md

@@ -5,7 +5,7 @@ As we have learned already, to be able to train neural networks efficiently we n
 * To operate on tensors, eg. to multiply, add, and compute some functions such as sigmoid or softmax
 * To compute gradients of all expressions, in order to perform gradient descent optimization
 
-## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/501)
+## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/105)
 
 While the `numpy` library can do the first part, we need some mechanism to compute gradients. In [our framework](../04-OwnFramework/OwnFramework.ipynb) that we have developed in the previous section we had to manually program all derivative functions inside the `backward` method, which does backpropagation. Ideally, a framework should give us the opportunity to compute gradients of *any expression* that we can define.
 
@@ -23,7 +23,7 @@ High-level API| [Keras](https://keras.io/) | [PyTorch Lightning](https://pytorch
 
 **High-level APIs** pretty much consider neural networks as a **sequence of layers**, and make constructing most of the neural networks much easier. Training the model usually requires preparing the data and then calling a `fit` function to do the job.
 
-The high-level API allows you to construct typical neural networks very quickly without worrying about lots of details. At the same time, low-level API offer much more control over the training process, and thus they are used a lot in research, when you are dealing with new neural network architectures. 
+The high-level API allows you to construct typical neural networks very quickly without worrying about lots of details. At the same time, low-level API offer much more control over the training process, and thus they are used a lot in research, when you are dealing with new neural network architectures.
 
 It is also important to understand that you can use both APIs together, eg. you can develop your own network layer architecture using low-level API, and then use it inside the larger network constructed and trained with the high-level API. Or you can define a network using the high-level API as a sequence of layers, and then use your own low-level training loop to perform optimization. Both APIs use the same basic underlying concepts, and they are designed to work well together.
 

lessons/3-NeuralNetworks/05-Frameworks/lab/README.md

@@ -4,7 +4,7 @@ Lab Assignment from [AI for Beginners Curriculum](https://github.com/microsoft/a
 
 ## Task
 
-Solve two classification problems using single- and multi-layered fully-connected networks using PyTorch or TensorFlow:
+Solve two classification problems using single and multi-layered fully-connected networks using PyTorch or TensorFlow:
 
 1. **[Iris classification](https://en.wikipedia.org/wiki/Iris_flower_data_set)** problem - an example of problem with tabular input data, which can be handled by classical machine learning. You goal would be to classify irises into 3 classes, based on 4 numeric parameters.
 1. **MNIST** handwritten digit classification problem which we have seen before.
@@ -14,4 +14,3 @@ Try different network architectures to achieve the best accuracy you can get.
 ## Stating Notebook
 
 Start the lab by opening [LabFrameworks.ipynb](LabFrameworks.ipynb)
-

lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md

@@ -41,3 +41,5 @@ Here is [a good blog post](https://medium.com/analytics-vidhya/talented-mr-1x1-c
 MobileNet is a family of models with reduced size, suitable for mobile devices. Use them if you are short in resources, and can sacrifice a little bit of accuracy. The main idea behind them is so-called **depthwise separable convolution**, which allows representing convolution filters by a composition of spatial convolutions and 1x1 convolution over depth channels. This significantly reduces the number of parameters, making the network smaller in size, and also easier to train with less data.
 
 Here is [a good blog post on MobileNet](https://medium.com/analytics-vidhya/image-classification-with-mobilenet-cc6fbb2cd470).
+
+## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/207)