I bought a Deep Learning textbook this week! Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville.
I bought it after a very thorough research on the contents and previous reviews. And actually it is a good choice. This book has developed to be one of the fundamental textbooks of Deep learning and is the latest of them.
I bought it after a very thorough research on the contents and previous reviews. And actually it is a good choice. This book has developed to be one of the fundamental textbooks of Deep learning and is the latest of them.
I am excited about the book when i started reading it. But i feel reading is very lonely, i want to say what is actually exciting. I want to help some one who wants to know what the book is about. So i thought of writing a chapter wise review of the book. I plan to release review of each chapter on Fridays starting today(31/01/2019). I will keep updating this post with the links to the review of the chapter. This is my first experience in technical book review and i am hoping to do my best.
| Chapter No | Name | Link | Published/Scheduled Date |
|---|---|---|---|
| 1 | Introduction | https://viswa10.blogspot.com/2020/01/deep-learning-goodfellow-et-al-chapter.html | 31/01/20 |
| 2 | Linear Algebra | https://viswa10.blogspot.com/2020/02/deep-learning-goodfellow-et-al-chapter.html | 16/02/20 |
| 3 | Probability and Information Theory | ------------- | 19/02/20 |
| 4 | Numerical Computation | ------------- | 21/02/20 |
| 5 | Machine Learning Basics | ------------- | 28/02/20 |
| 6 | Deep Feedforward Networks | ------------- | 06/03/20 |
| 7 | Regularization for Deep Learning | ------------- | 13/03/20 |
| 8 | Optimization for training Deep Models | ------------- | 20/03/20 |
| 9 | Convolutional Networks | ------------- | 27/03/20 |
| 10 | Sequence Modeling: Recurrent and Recursive Nets | ------------- | 03/04/20 |
| 11 | Practical Methodology | ------------- | 10/04/20 |
| 12 | Applications | ------------- | 17/04/20 |
| 13 | Linear Factor Models | ------------- | 24/04/20 |
| 14 | Autoencoders | ------------- | 01/05/20 |
| 15 | Representation Learning | ------------- | 08/05/20 |
| 16 | Structured Probabilistic Models for Deep Learning | ------------- | 15/05/20 |
| 17 | Monte Carlo Methods | ------------- | 22/05/20 |
| 18 | Confronting the Partition Function | ------------- | 29/05/20 |
| 19 | Approximate Inference | ------------- | 05/06/20 |
| 20 | Deep Generative Models | ------------- | 12/06/20 |
| 1-20 | Whole Book Review | ------------- | 15/06/20 |
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