CS231n: Convolutional Neural Networks for Visual Recognition
Event Type | Date | Description | Course Materials |
Lecture 1 | Tuesday April 4 | Course Introduction Computer vision overview Historical context Course logistics | |
Lecture 2 | Thursday April 6 | Image Classification The data-driven approach K-nearest neighbor Linear classification I | |
Lecture 3 | Tuesday April 11 | Loss Functions and Optimization Linear classification II Higher-level representations, image features Optimization, stochastic gradient descent | |
Lecture 4 | Thursday April 13 | Introduction to Neural Networks Backpropagation Multi-layer Perceptrons The neural viewpoint | [backprop notes][linear backprop example] [derivatives notes] (optional) [Efficient BackProp] (optional) |
Lecture 5 | Tuesday April 18 | Convolutional Neural Networks History Convolution and pooling ConvNets outside vision | |
Lecture 6 | Thursday April 20 | Training Neural Networks, part I Activation functions, initialization, dropout, batch normalization | Neural Nets notes 1Neural Nets notes 2 tips/tricks: [1], [2], [3] (optional) Deep Learning [Nature] (optional) |
A1 Due | Thursday April 20 | Assignment #1 due kNN, SVM, SoftMax, two-layer network | |
Lecture 7 | Tuesday April 25 | Training Neural Networks, part II Update rules, ensembles, data augmentation, transfer learning | |
Proposal due | Tuesday April 25 | Couse Project Proposal due | |
Lecture 8 | Thursday April 27 | Deep Learning Software Caffe, Torch, Theano, TensorFlow, Keras, PyTorch, etc | |
Lecture 9 | Tuesday May 2 | CNN Architectures AlexNet, VGG, GoogLeNet, ResNet, etc | |
Lecture 10 | Thursday May 4 | Recurrent Neural Networks RNN, LSTM, GRU Language modeling Image captioning, visual question answering Soft attention | DL book RNN chapter (optional) |
A2 Due | Thursday May 4 | Assignment #2 due Neural networks, ConvNets | |
Midterm | Tuesday May 9 | In-class midterm Location: Various (not | |
Lecture 11 | Thursday May 11 | Detection and Segmentation Semantic segmentation Object detection Instance segmentation | |
Lecture 12 | Tuesday May 16 | Visualizing and Understanding Feature visualization and inversion Adversarial examples DeepDream and style transfer | |
Milestone | Tuesday May 16 | Course Project Milestone due | |
Lecture 13 | Thursday May 18 | Generative Models PixelRNN/CNN Variational Autoencoders Generative Adversarial Networks | |
Lecture 14 | Tuesday May 23 | Deep Reinforcement Learning Policy gradients, hard attention Q-Learning, Actor-Critic | |
Guest Lecture | Thursday May 25 | Invited Talk: Song Han Efficient Methods and Hardware for Deep Learning | |
A3 Due | Friday May 26 | Assignment #3 due | |
Guest Lecture | Tuesday May 30 | Invited Talk: Ian Goodfellow Adversarial Examples and Adversarial Training | |
Lecture 16 | Thursday June 1 | Student spotlight talks, conclusions | [slides] |
Poster Due | Monday June 5 | Poster PDF due | |
Poster Presentation | Tuesday June 6 | | |
Final Project Due | Monday June 12 | Final course project due date |
