CS231n Convolutional Neural Networks for Visual Recognition

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

Table of Contents: Introduction Simple expressions, interpreting the gradient Compound expressions, chain rule, backpropagation Intuitive understanding of backpropagation Modularity: Sigmoid example Backprop in practice: Staged computation Patterns in backward flow Gradients for vectorized operations Summary Introduction Motivation . In this section we will develop expertise with an intuitive understanding of backpropagation , which is a way of computing gradients of expressions through recursive application of chain rule . Understanding of this process and its subtleties is critical for you t

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