1_softmax.ipynb
Softmax Classification (with Cross-Entropy Loss)
Implement a fully-vectorized loss function for the Softmax classifier Implement the fully-vectorized expression for its analytic gradient Check your implementation with numerical gradient Use a validation set to tune the learning rate and regularization strength Optimize the loss function with SGD Visualize the final learned weights
2_two_layer_net.ipynb
Develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset.
3_features.ipynb
Show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.