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 |
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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 |
|
Lecture | Sep 27 |
Introduction to ConvNets (architectures – AlexNet, VGG, Inception, ResNet, DenseNet – loss surface,…) (cont.) Training ConvNets |
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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 |
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Lecture | Nov 15 |
Understanding Neural Nets as Splines (cont.) Deep Reinforcement Learning Final Project Proposal due |
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Lecture | Nov 22 | Thanksgiving – No class | |
Lecture | Nov 29 |
Guest Lecture: Alan Lockett (Company: CS DISCO) NLP + Deep Learning, Large Language Models |
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Final Project |
Dec 4 & 5 Dec 12, 23:59 |
Project Presentation
Project Report Due |