Unless otherwise specified the lectures are Tuesday and Thursday 12pm to 1:20pm in the NVIDIA Auditorium in the Huang Engineering Center. (map)
Discussion sections will be Fridays 12:30pm to 1:20pm in Skilling Auditorium.
(map)
This is the syllabus for the Spring 2018 iteration of the course.
The syllabus for the Spring 2017, Winter 2016
and Winter 2015 iterations of this course
are still available.
| Event Type | Date | Description | Course Materials |
|---|---|---|---|
| Lecture 1 | Tuesday April 3 |
Course Introduction Computer vision overview Historical context Course logistics |
[slides] |
| Lecture 2 | Thursday April 5 |
Image Classification The data-driven approach K-nearest neighbor Linear classification I |
[slides]
[python/numpy tutorial] [image classification notes] [linear classification notes] |
| Discussion Section | Friday April 6 |
Python / numpy / Google Cloud | [python/numpy notebook] |
| Lecture 3 | Tuesday April 10 |
Loss Functions and Optimization Linear classification II Higher-level representations, image features Optimization, stochastic gradient descent |
[slides]
[linear classification notes] [optimization notes] |
| Lecture 4 | Thursday April 12 |
Introduction to Neural Networks Backpropagation Multi-layer Perceptrons The neural viewpoint |
[slides]
[backprop notes] [linear backprop example] [derivatives notes] (optional) [Efficient BackProp] (optional) related: [1], [2], [3] (optional) |
| Discussion Section | Friday April 13 |
Backpropagation | [slides] |
| Lecture 5 | Tuesday April 17 |
Convolutional Neural Networks History Convolution and pooling ConvNets outside vision |
[slides]
ConvNet notes |
| A1 Due | Wednesday April 18 |
Assignment #1 due kNN, SVM, SoftMax, two-layer network |
[Assignment #1] |
| Lecture 6 | Thursday April 19 |
Training Neural Networks, part I Activation functions, initialization, dropout, batch normalization |
[slides]
Neural Nets notes 1 Neural Nets notes 2 Neural Nets notes 3 tips/tricks: [1], [2], [3] (optional) Deep Learning [Nature] (optional) |
| Discussion Section | Friday April 20 |
Tips and tricks for tuning NNs | [slides] |
| Lecture 7 | Tuesday April 24 |
Training Neural Networks, part II Update rules, ensembles, data augmentation, transfer learning |
[slides]
Neural Nets notes 3 |
| Proposal due | Wednesday April 25 |
Project Proposal due | [proposal description] |
| Lecture 8 | Thursday April 26 |
Deep Learning Hardware and Software CPUs, GPUs, TPUs PyTorch, TensorFlow Dynamic vs Static computational graphs |
[slides] |
| Discussion Section | Friday April 27 |
PyTorch / Tensorflow | [pytorch notebook] |
| Lecture 9 | Tuesday May 1 |
CNN Architectures AlexNet, VGG, GoogLeNet, ResNet, etc |
[slides]
AlexNet, VGGNet, GoogLeNet, ResNet |
| A2 Due | Wednesday May 2 |
Assignment #2 due Neural networks, ConvNets |
[Assignment #2] |
| Lecture 10 | Thursday May 4 |
Recurrent Neural Networks RNN, LSTM, GRU Language modeling Image captioning, visual question answering Soft attention |
[slides]
DL book RNN chapter (optional) min-char-rnn, char-rnn, neuraltalk2 |
| Discussion Section | Friday May 4 |
Midterm Review | [slides] |
| Midterm | Tuesday May 8 |
In-class midterm Location: Various (see Piazza for more details). |
SCPD Midterm Info |
| Lecture 11 | Thursday May 10 |
Detection and Segmentation Semantic segmentation Object detection Instance segmentation |
[slides]
|
| Discussion Section | Friday May 11 |
Practical Object Detection and Segmentation | [slides] |
| Lecture 12 | Tuesday May 15 |
Generative Models PixelRNN/CNN Variational Autoencoders Generative Adversarial Networks |
[slides]
|
| Milestone | Wednesday May 16 |
Project Milestone due | |
| Lecture 13 | Thursday May 17 |
Visualizing and Understanding Feature visualization and inversion Adversarial examples DeepDream and style transfer |
[slides]
DeepDream neural-style fast-neural-style |
| Lecture 14 | Tuesday May 22 |
Deep Reinforcement Learning Policy gradients, hard attention Q-Learning, Actor-Critic |
[slides]
|
| A3 Due | Wednesday May 23 |
Assignment #3 due | [Assignment #3] |
| Lecture 15 Guest Lecture |
Thursday May 24 |
Invited Talk: Andrej Karpathy |
[slides]
|
| Discussion Section | Friday May 25 |
Weak Supervision | [slides] |
| Lecture 16 Guest Lecture |
Tuesday May 29 |
Invited Talk: Jitendra Malik
|
[slides]
|
| Lecture 17 | Thursday May 31 |
Student spotlight talks, conclusions | [slides] |
| Discussion Section | Friday June 1 |
Video Understanding | [slides] |
| Final Project Due | Thursday June 7 |
Project Report due | |
| Poster Session | Tuesday June 12 |
Jen-Hsun Huang Engineering Center 12:00 pm to 3:15 pm |