Introductions

• Slides Session 01

• * Deep Learning Book - Part I: Applied Math and Machine Learning Basics

MLPs, Gradient Descent & Backpropagation

• Slides Session 02

• Deep Learning Book - Chapter 6: Deep Feedforward Networks

CNNs

• Recap Session 02
• Slides Session 03
• CNN quiz on Mattermost

• Deep Learning Book - Chapter 9: Convolutional Networks, Sections 9.1 to 9.5

CNNs (cont.)

• Recap Session 03
• Slides Session 04

• Deep Learning Book - Chapter 9: Convolutional Networks, remaining sections
• additional paper reading

CNNs (cont.)

• Recap Session 04
• Slides Session 05

• paper reading

RNNs

• Slides Session 06

• Deep Learning Book - Chapter 10: Sequence Modeling: Recurrent and Recursive Nets, Sections 10.1 to 10.5

RNNs (cont.)

• Recap Session 06
• Slides Session 07

• Deep Learning Book - Chapter 10: Sequence Modeling: Recurrent and Recursive Nets, Sections 10.6, 10.7 and 10.10 to 10.12

Attention & Memory

• Reading Assignment

Practical Methodology
no lecture on Dec 10

• Deep Learning Book - Chapter 11: Practical Methodology
• Goodfellow's talk on YouTube

Regularization

• Recap Session 09
• Slides Session 09

• Deep Learning Book - Chapter 7: Regularization

Optimization

• Recap Session 10
• Slides Session 10

• Deep Learning Book - Chapter 8: Optimization (except Section 8.6)

Autoencoders

• Slides Session 11

• Deep Learning Book - Chapter 14: Autoencoders

Introspection

• Slides Session 12

• Reading Assignment

Revision (selected topics)

• Slides Session 13

• Reading Assignment

Model Compression & Transfer Learning

• Reading Assignment