CS231n - Convolutional Neural Networks for Vision Recognition

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斯坦福大学14-15年秋季课程《CS231n: Convolutional Neural Networks for Visual Recognition》的讲义材料，英文版。我把它做成了PDF，带目录，方便阅读和打印。

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Convolutional Neural Networks

for Visual Recognition

Feifei Li

CS231n, Stanford Open Course

Edited by fengfu-chris, email: fengfu0527@gmail.com

Contents

Part I: Neural Networks

1.1 - Image Classification: Data-driven Approach, k-Nearest Neighbor, train/val/test splits ------- 3

1.2 - Linear classification: Support Vector Machine, Softmax ------------------------------------------ 19

1.3 - Optimization: Stochastic Gradient Descent ---------------------------------------------------------- 37

1.4 - Backpropagation, Intuitions ---------------------------------------------------------------------------- 51

1.5 - Neural Networks Part 1: Setting up the Architecture ----------------------------------------------- 60

1.6 - Neural Networks Part 2: Setting up the Data and the Loss ---------------------------------------- 73

1.7 - Neural Networks Part 3: Learning and Evaluation -------------------------------------------------- 89

1.8 - Putting it together: Minimal Neural Network Case Study ---------------------------------------- 109

Part II: Convolutional Neural Networks

2.1 - Convolutional Neural Networks: Architectures, Convolution & Pooling Layers ------------- 123

2.2 - Understanding and Visualizing Convolutional Neural Networks ------------------------------- 146

2.3 - Transfer Learning and Fine-tuning Convolutional Neural Networks --------------------------- 152

2.4 - ConvNet Tips and Tricks: squeezing out the last few percent ----------------------------------- 155

Part X: Preparation

x.1 - Python Numpy Tutorial ------------------------------------------------------------------------------- 156

x.2 - IPython Tutorial ---------------------------------------------------------------------------------------- 182

x.3 - Terminal ------------------------------------------------------------------------------------------------- 186

These notes accompany the Stanford CS class CS231n: Convolutional Neural Networks
for Visual Recognition. Feel free to ping @karpathy if you spot any mistakes or issues,
or submit a pull request to our git repo. 
We encourage the use of the hypothes.is extension to annote comments and discuss
these notes inline.
Assignments
Assignment #1: Image Classification, kNN, SVM, Softmax
Assignment #2: Neural Networks, ConvNets I
Assignment #3: ConvNets II, Transfer Learning, Visualization
Module 0: Preparation
Python / Numpy Tutorial
IPython Notebook Tutorial
Terminal.com Tutorial
Module 1: Neural Networks
Image Classification: Data-driven Approach, k-Nearest Neighbor,
train/val/test splits
L1/L2 distances, hyperparameter search, cross-validation
Linear classification: Support Vector Machine, Softmax
parameteric approach, bias trick, hinge loss, cross-entropy loss, L2 regularization, web
demo
Optimization: Stochastic Gradient Descent
optimization landscapes, local search, learning rate, analytic/numerical gradient
Backpropagation, Intuitions
chain rule interpretation, real-valued circuits, patterns in gradient flow
Neural Networks Part 1: Setting up the Architecture
CS231n Convolutional Neural Networks for Visual
Recognition
1