CS231n Convolutional Neural Networks for Visual Recognition

Course materials and notes for Stanford class CS231n: Deep Learning for Computer Vision.

Table of Contents: Gradient checks Sanity checks Babysitting the learning process Loss function Train/val accuracy Weights:Updates ratio Activation/Gradient distributions per layer Visualization Parameter updates First-order (SGD), momentum, Nesterov momentum Annealing the learning rate Second-order methods Per-parameter adaptive learning rates (Adagrad, RMSProp) Hyperparameter Optimization Evaluation Model Ensembles Summary Additional References Learning In the previous sections we’ve discussed the static parts of a Neural Networks: how we can set up the network connectivity, the data, and th

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