Lecture Schedule

Date Subject Additional Readings
1/16 History of Deep Learning Goodfellow Ch. 1-5
1/23 Introduction to Pytorch Goodfellow Ch. 6
Original UAF Paper
Depth vs. Width (Theorem 1)
1/28 Introduction to Neural Networks Goodfellow Ch. 7-8
Literature Review
1/30 Challenges in Training Neural Nets Goodfellow Ch. 7-8
2/4 Deep Vs. Shallow Learning Goodfellow Ch. 9
Do Deep Nets Really Need to be Deep
Deep vs. Shallow Complexity
Depth vs. Width (Theorem 7)
2/6 Convolutional Neural Nets Goodfellow Ch. 9
2/11 CNNs and Capsule Nets Goodfellow Ch. 20
Dynamic Routing
2/13 Problems with CNNs and recent innovations Goodfellow Ch. 20
2/18 Vector semantics and CNNs for NLP Goodfellow Ch. 20
2/25 Autoencoders Goodfellow Ch. 20
2/27 GANs Goodfellow Ch. 20
3/11 RNNs and LSTMs Goodfellow Ch. 10
The Unreasonable Effectiveness of Recurrent Neural Networks
3/13 Generative RNNs
3/18 Attention Neural Machine Translation by Jointly Learning to Align and Translate
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Grammar as a Foreign Language
Teaching Machines to Read and Comprehend
3/20 Attention and Transformers
3/25 Reinforcement Learning, Part I
4/01 Hyperparameters and learning to learn
4/08 Neuroscience, Part I
4/10 Neuroscience, Part II
4/15 Deep Learning & Society
4/17 Applications of Deep Learning
4/24 Sarcasm Presentation