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| Q2 | Training a Support Vector Machine |[``svm.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment1/svm.ipynb)|
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| Q3 | Implement a Softmax classifier |[``softmax.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment1/softmax.ipynb)|
| Q1 |Image Captioning with Vanilla RNNs |[``RNN_Captioning.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment3/RNN_Captioning.ipynb)|
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| Q2 |Image Captioning with LSTMs |[``LSTM_Captioning.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment3/LSTM_Captioning.ipynb)|
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| Q3 | Network Visualization: Saliency maps, Class Visualization, and Fooling Images |[``NetworkVisualization-PyTorch.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment3/NetworkVisualization-PyTorch.ipynb)|
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| Q4 |Style Transfer |[``StyleTransfer-PyTorch.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment3/StyleTransfer-PyTorch.ipynb)|
The list below provides the most useful external resources that helped me to clarify and understand deeply some ambiguous topics encountered in the lectures. Note that those are only the most important ones, that is, completely understanding them will maybe require checking other -not mentioned- resources.
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- Convolutional Neural Networks (CNNs).
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- CNNs implementation from scratch in Python [[Part 1]](https://victorzhou.com/blog/intro-to-cnns-part-1/)[[Part 2]](https://victorzhou.com/blog/intro-to-cnns-part-2/).
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-[A guide to receptive field arithmetic for CNNs](https://medium.com/mlreview/a-guide-to-receptive-field-arithmetic-for-convolutional-neural-networks-e0f514068807).
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- Normalization layers.
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-[Understanding the backward pass through Batch Normalization Layer](https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html) (Staged computation method).
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-[Deriving the Gradient for the Backward Pass of Batch Normalization](https://kevinzakka.github.io/2016/09/14/batch_normalization/) (Gradient derivation method).
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-[Group Normalization - The paper](https://arxiv.org/abs/1803.08494) (Concept and implementation well explained).
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- Principal Component Analysis (PCA).
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-[Principal Component Analysis (PCA) from Scratch](https://drscotthawley.github.io/blog/2019/12/21/PCA-From-Scratch.html) (Covariance matrix method).
-[Variational Autoencoder Demystified With PyTorch Implementation](https://towardsdatascience.com/variational-autoencoder-demystified-with-pytorch-implementation-3a06bee395ed).
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- Generative Adversarial Networks (GANs).
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-[GANs from Scratch: A deep introduction, with code in PyTorch](https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f).
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-[GAN objective function origin explanation](https://ai.stackexchange.com/a/13038).
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@@ -21,20 +21,20 @@ In this assignment you will practice putting together a simple image classificat
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## Q1: k-Nearest Neighbor classifier
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The notebook [``knn.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment1/knn.ipynb) will walk you through implementing the kNN classifier.
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The notebook [``knn.ipynb``](knn.ipynb) will walk you through implementing the kNN classifier.
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## Q2: Training a Support Vector Machine
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The notebook [``svm.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment1/svm.ipynb) will walk you through implementing the SVM classifier.
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The notebook [``svm.ipynb``](svm.ipynb) will walk you through implementing the SVM classifier.
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## Q3: Implement a Softmax classifier
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The notebook [``softmax.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment1/softmax.ipynb) will walk you through implementing the Softmax classifier.
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The notebook [``softmax.ipynb``](softmax.ipynb) will walk you through implementing the Softmax classifier.
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## Q4: Two-Layer Neural Network
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The notebook [``two_layer_net.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment1/two_layer_net.ipynb) will walk you through the implementation of a two-layer neural network classifier.
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The notebook [``two_layer_net.ipynb``](two_layer_net.ipynb) will walk you through the implementation of a two-layer neural network classifier.
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## Q5: Higher Level Representations: Image Features
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The notebook [``features.ipynb``](https://github.com/seloufian/Deep-Learning-Computer-Vision/blob/master/cs231n/assignment1/features.ipynb) will examine the improvements gained by using higher-level representations as opposed to using raw pixel values.
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The notebook [``features.ipynb``](features.ipynb) will examine the improvements gained by using higher-level representations as opposed to using raw pixel values.
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