CS231n: Convolutional Neural Networks for Visual Recognition Spring 2017 英文课程大纲

This is the syllabus for the Spring 2017 iteration of the course. 

Event Type	Date	Description	Course Materials
Lecture 1	Tuesday  April 4	Course Introduction  Computer vision overview  Historical context  Course logistics	[slides] [video]
Lecture 2	Thursday  April 6	Image Classification  The data-driven approach  K-nearest neighbor  Linear classification I	[slides] [video]  [python/numpy tutorial] [image classification notes] [linear classification notes]
Lecture 3	Tuesday  April 11	Loss Functions and Optimization  Linear classification II Higher-level representations, image features Optimization, stochastic gradient descent	[slides] [video]  [linear classification notes] [optimization notes]
Lecture 4	Thursday  April 13	Introduction to Neural Networks  Backpropagation Multi-layer Perceptrons The neural viewpoint	[slides] [video]  [backprop notes] [linear backprop example] [derivatives notes] (optional)  [Efficient BackProp] (optional) related: [1], [2], [3] (optional)
Lecture 5	Tuesday  April 18	Convolutional Neural Networks  History  Convolution and pooling  ConvNets outside vision	[slides] [video]  ConvNet notes
Lecture 6	Thursday  April 20	Training Neural Networks, part I  Activation functions, initialization, dropout, batch normalization	[slides] [video]  Neural Nets notes 1 Neural Nets notes 2 Neural Nets notes 3 tips/tricks: [1], [2], [3] (optional)  Deep Learning [Nature] (optional)
A1 Due	Thursday  April 20	Assignment #1 due  kNN, SVM, SoftMax, two-layer network	[Assignment #1]
Lecture 7	Tuesday  April 25	Training Neural Networks, part II  Update rules, ensembles, data augmentation, transfer learning	[slides] [video]  Neural Nets notes 3
Proposal due	Tuesday  April 25	Couse Project Proposal due	[proposal description]
Lecture 8	Thursday  April 27	Deep Learning Software  Caffe, Torch, Theano, TensorFlow, Keras, PyTorch, etc	[slides] [video]
Lecture 9	Tuesday  May 2	CNN Architectures  AlexNet, VGG, GoogLeNet, ResNet, etc	[slides] [video]  AlexNet, VGGNet, GoogLeNet, ResNet
Lecture 10	Thursday  May 4	Recurrent Neural Networks  RNN, LSTM, GRU  Language modeling  Image captioning, visual question answering  Soft attention	[slides] [video]  DL book RNN chapter (optional) min-char-rnn, char-rnn, neuraltalk2
A2 Due	Thursday  May 4	Assignment #2 due  Neural networks, ConvNets	[Assignment #2]
Midterm	Tuesday  May 9	In-class midterm Location: Various (not our usual classroom)
Lecture 11	Thursday  May 11	Detection and Segmentation  Semantic segmentation  Object detection  Instance segmentation	[slides] [video]
Lecture 12	Tuesday  May 16	Visualizing and Understanding  Feature visualization and inversion  Adversarial examples  DeepDream and style transfer	[slides] [video]  DeepDream neural-style fast-neural-style
Milestone	Tuesday  May 16	Course Project Milestone due
Lecture 13	Thursday  May 18	Generative Models  PixelRNN/CNN  Variational Autoencoders  Generative Adversarial Networks	[slides] [video]
Lecture 14	Tuesday  May 23	Deep Reinforcement Learning  Policy gradients, hard attention  Q-Learning, Actor-Critic	[slides] [video]
Guest Lecture	Thursday  May 25	Invited Talk: Song Han  Efficient Methods and Hardware for Deep Learning	[slides] [video]
A3 Due	Friday  May 26	Assignment #3 due	[Assignment #3]
Guest Lecture	Tuesday  May 30	Invited Talk: Ian Goodfellow  Adversarial Examples and Adversarial Training	[slides] [video]
Lecture 16	Thursday  June 1	Student spotlight talks, conclusions	[slides]
Poster Due	Monday  June 5	Poster PDF due	[poster description]
Poster Presentation	Tuesday June 6
Final Project Due	Monday  June 12	Final course project due date	[reports]