CS 342 - Neural networks - Fall 2017

TTh 10am-11am GDC 5.302
Fr 1pm-2pm / 2pm-3pm CBA 4.326

instructor Philipp Krähenbühl ( philkr (at) utexas.edu )
office hours Tue 11am-12pm GDC 4.824
Please only visit during office hours or with appointment (or if I didn’t reply your email after the third try)

TA Thomas Crosley ( crosleythomas (at) utexas.edu )
TA hours Mondays 5-5:30pm at TA Desk 1, Thursdays 11:30am-Noon at TA Desk 1

We will use canvas for questions and homework.


class section

Date Topic Slides Corresponding book chapters (optional see below) Notes and due dates
Aug 31 Introduction intro Background: Ch 1-5  
Sep 1 Installing tensorflow and building the first compute graph     HW1 out
Sep 5 Single layer networks slides visualizations    
Sep 7 Stochastic gradient descent slides 5.9 HW1 due 11:59pm solution
Sep 8 Logistic regression and MLP Tutorial   HW2 out
Sep 12 Multi-layer networks and back propagation slides Ch 6  
Sep 14 Loss functions slides   HW2 due 11:59pm solution
Sep 15 Regression vs classification Section Practice Section Master   HW3 out
Sep 19 Activation functions + Initialization slides Ch 6  
Sep 21 Convolutions + Pooling slides 9.1-9.3 HW3 due 11:59pm solution
Sep 22 ConvNets     HW4 out Section
Sep 26 Early stopping, weight regularization, dropout slides 7.1-7.3, 7.8, 7.12  
Sep 28 Augmentation, batch normalization, parameter sharing, ensembles slides 7.4, 7.9, 7.11 HW4 due 11:59pm solution
Sep 29 Regularization and normalization     HW5 out Section
Oct 3 Popular Architectures I: LeNet, AlexNet, VGG slides    
Oct 5 Popular Architectures II: GoogLeNet, ResNet, DenseNet slides   HW5 due 11:59pm solution
Oct 6 ResNets     HW6 out Section
Oct 10 Object detection slides    
Oct 12 Segmentation slides   HW6 due 11:59pm solution
Oct 13 Fully convolutional networks     HW7 out Section
Oct 17 Visualization and understanding slides    
Oct 19 Generative models slides 20.10, 20.12, 20.13 HW7 due 11:59pm solution
Oct 20 Upconvolution and deep rendering     HW8 out
Oct 24 Recurrent Network I: RNN, LSTM, GRU slides 10.1-10.11  
Oct 26 Recurrent Network II: ConvNets for recurrent tasks slides   HW8 due 11:59pm solution
Oct 27 Future prediction     HW9 out Section
Oct 31 Deep Reinforcement Learning I: Policy gradients slides    
Nov 2 Deep Reinforcement Learning II: Q-learning and Actor critic slides   HW9 out 11:59pm (moved to Tuesday)
Nov 3 Acting through future prediction      
Nov 7 Deep Reinforcement Learning III: Direct future prediction slides    
Nov 9 Deep Reinforcement Learning IV: Gradient free optimization slides    
Nov 10 Gradient free optimization     HW9 solution HW10 out
Nov 14 Embedding learning slides    
Nov 16 Style transfer     HW10 due 11:59pm
Nov 17 Driving in SuperTuxKart     Final project out
Nov 21 Language slides    
Nov 23 no class      
Nov 24 no class      
Nov 28 Adversarial examples slides 7.13  
Nov 30 Negative results      
Dec 1 Final project Q/A      
Dec 5 Final project flash presentations     Final project due 11:59pm
Dec 7 Final project competition      
Dec 8 Nagging TA for better grade      


Class overview

Goals of the class

After this class you should be able to


There will be a homework assignment every week, but we will keep them small and manageable. Most of the homework will be graded automatically, you’ll have access to a partial grader while you’re working on the assignment. We will use the same grader, but different test data for your final grade. You’ll submit your homework through canvas.

Late policy:

Expected workload

Estimates of required effort to pass the class are:

Course material

The course should be self contained, but if you need additional reading material consult Deep learning, Goodfellow, Bengio and Courville 2016 www.deeplearningbook.org.


Syllabus subject to change.