Schedule and Syllabus

Note: This syllabus is subject to change based on the needs of the class

Event Type Date   Description Course Material
Lecture Aug 23 Overview of Deep Learning Applications

[slides F22]

 

Lecture Aug 23, Aug 30

History of Deep Learning

Assignment #0 available

[slides F22]
Lecture Sep 6 Stochastic Gradient Descent, Neural Networks, & Backpropagation [slides F22]
Lecture Sep 13

Neural Networks & Backpropagation (cont.)

Asssigment #0 due

 

[slides F22]
Lecture Sep 20

Neural Networks & Backpropagation (cont.)

Introduction to ConvNets (architectures – AlexNet, VGG,  Inception, ResNet, DenseNet – loss surface,…)

Guest Lecture: Erik Enquist, Rice RCSG

Assignment #1 available

[slides F22]

[slides F22]

Lecture Sep 27

Introduction to ConvNets (architectures – AlexNet, VGG,  Inception, ResNet, DenseNet – loss surface,…) (cont.)

Training ConvNets

[slides F22]

[slides F22]

Lecture Oct 4

Species of ConvNets 

Assignment #1 due                                        

[slides F22]
Lecture Oct 11

Understanding and Visualizing Convnets &                               

Introduction to Recurrent Neural Networks

 

[slides F22]
Lecture Oct 18

Recurrent Neural Networks Applications

Assignment #2 available

[slides F22]
Lecture Oct 25 Recurrent Neural Networks Applications (cont.) [slides F22]
Lecture Nov 1

Recurrent Neural Networks Applications (cont.)

Assignment #2 due

[slides F22]
Lecture Nov 8

Deep Reinforcement Learning

Understanding Neural Nets as Splines

[slides F22]

[slides F22]

Lecture Nov 15

Understanding Neural Nets as Splines (cont.)

Deep Reinforcement Learning

Final Project Proposal due

[slides F22]

[slides F22]

Lecture Nov 22 Thanksgiving  – No class
Lecture Nov 29

Guest Lecture:  Alan Lockett (Company: CS DISCO)

NLP + Deep Learning, Large Language Models

Final Project

Dec 4 & 5

Dec 12, 23:59

Project Presentation

 

Project Report Due