We are going to review each chapter of the book:
http://www.deeplearningbook.org/
For participants to gain the most experience and understanding of the material, having a volunteer presenter each week is an invaluable asset. So we have decided that one volunteer each week take on the challenge of presenting their findings from the material to the rest of the group.
This presentation can be as short as 10 min or as long as an hour depending on the depth of the materials covered. It is also up to the presenter if they would like to prepare slides or give a free form talk on the subject. We simply ask that the volunteer does not read directly from the book as their "presentation".
Sign up below and please include links to any supplemental material, research papers, or your slides that are cited during your presentation.
Once the presenter has given their talk, we will open up the floor for discussion and questions from the audience.
Looking forward to learning with you!
PS: This idea was pretty popular on reddit
| Chapter | Topic |
|---|---|
| 01 - 02 | Introduction and Linear Algebra |
| 03 | Probability and Information Theory |
| 04 | Numerical Computation |
| 05 | Machine Learning Basics |
| 06 | Deep Feedforward Networks |
| 07 | Regularization for Deep Learning |
| 08 | Optimization for Training Deep Models |
| 09 | Convolutional Networks |
| 10 | Sequence Modeling: Recurrent and Recursive Nets |
| 11 | Practical Methodology |
| 12 | Applications |
| 13 | Linear Factor Models |
| 14 | Autoencoders |
| 15 | Representation Learning |
| 16 | Structured Probabilistic Models for Deep Learning |
| 17 | Monte Carlo Methods |
| 18 | Confronting the Partition Function |
| 19 | Approximate Inference |
| 20 | Deep Generative Models |