The notebook knn.ipynb will walk you through implementing the kNN classifier.
The notebook svm.ipynb will walk you through implementing the SVM classifier.
The notebook softmax.ipynb will walk you through implementing the Softmax classifier.
The notebook two_layer_net.ipynb will walk you through the implementation of a two-layer neural network classifier.
The notebook features.ipynb will examine the improvements gained by using higher-level representations as opposed to using raw pixel values.
Assignment #2: Fully Connected and Convolutional Nets, Batch Normalization, Dropout, Pytorch & Network Visualization
The notebook FullyConnectedNets.ipynb will have you implement fully connected networks of arbitrary depth. To optimize these models you will implement several popular update rules.
In notebook BatchNormalization.ipynb you will implement batch normalization, and use it to train deep fully connected networks.
The notebook Dropout.ipynb will help you implement dropout and explore its effects on model generalization.
In the notebook ConvolutionalNetworks.ipynb you will implement several new layers that are commonly used in convolutional networks.
For this part, you will be working with PyTorch, a popular and powerful deep learning framework.
Open up PyTorch.ipynb. There, you will learn how the framework works, culminating in training a convolutional network of your own design on CIFAR-10 to get the best performance you can.
The notebook Network_Visualization.ipynb will introduce the pretrained SqueezeNet model, compute gradients with respect to images, and use them to produce saliency maps and fooling images.