# Deep-Learning-Book-Chapter-Summaries

**Repository Path**: rwang0417/Deep-Learning-Book-Chapter-Summaries

## Basic Information

- **Project Name**: Deep-Learning-Book-Chapter-Summaries
- **Description**: Attempting to make the Deep Learning Book easier to understand.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-24
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Deep-Learning-Book-Chapter-Summaries
This repository provides a summary for each chapter of the Deep Learning [book](http://deeplearningbook.org) by Ian Goodfellow, Yoshua Bengio and Aaron Courville and attempts to explain some of the concepts in greater detail.

## Chapters

- **Part I: Applied Math and Machine Learning Basics**
  - Chapter 2: Linear Algebra [[chapter](http://www.deeplearningbook.org/contents/linear_algebra.html)]
  - Chapter 3: Probability and Information Theory [[chapter](http://www.deeplearningbook.org/contents/prob.html)]
  - Chapter 4: Numerical Computation [[chapter](http://www.deeplearningbook.org/contents/numerical.html)]
  - Chapter 5: Machine Learning Basics [[chapter](http://www.deeplearningbook.org/contents/ml.html)]
  
- **Part II: Modern Practical Deep Networks**
  - Chapter 6: Deep Feedforward Networks [[chapter](http://www.deeplearningbook.org/contents/mlp.html)]
  - Chapter 7: Regularization for Deep Learning [[chapter](http://www.deeplearningbook.org/contents/regularization.html)]
  - Chapter 8: Optimization for Training Deep Models [[chapter](http://www.deeplearningbook.org/contents/optimization.html)]
  - Chapter 9: Convolutional Networks [[chapter](http://www.deeplearningbook.org/contents/convnets.html)]
  - Chapter 10: Sequence Modeling: Recurrent and Recursive Nets [[chapter](http://www.deeplearningbook.org/contents/rnn.html)]
  - Chapter 11: Practical Methodology [[chapter](http://www.deeplearningbook.org/contents/guidelines.html)]
  - Chapter 12: Applications [[chapter](http://www.deeplearningbook.org/contents/applications.html)]
  
- **Part III: Deep Learning Research**
  - Chapter 13: Linear Factor Models [[chapter](http://www.deeplearningbook.org/contents/linear_factors.html)]
  - Chapter 14: Autoencoders [[chapter](http://www.deeplearningbook.org/contents/autoencoders.html)]
  - Chapter 15: Representation Learning [[chapter](http://www.deeplearningbook.org/contents/representation.html)]
  - Chapter 16: Structured Probabilistic Models for Deep Learning [[chapter](http://www.deeplearningbook.org/contents/graphical_models.html)]
  - Chapter 17: Monte Carlo Methods [[chapter](http://www.deeplearningbook.org/contents/monte_carlo.html)]
  - Chapter 18: Confronting the Partition Function [[chapter](http://www.deeplearningbook.org/contents/partition.html)]
  - Chapter 19: Approximate Inference [[chapter](http://www.deeplearningbook.org/contents/inference.html)]
  - Chapter 20: Deep Generative Models [[chapter](http://www.deeplearningbook.org/contents/generative_models.html)]
  
## Contributors
- [Aman Dalmia](https://github.com/dalmia)
- [Ameya Godbole](https://github.com/ameyagodbole)

## Contributing

Please feel free to open a Pull Request to contribute a summary for the chapters 5, 6 and 12 as we might not be able to cover them owing to other commitments. Also, if you think there's any section that requires more/better explanation, please use the issue tracker to let us know about the same.

## Support 

If you like this repo and find it useful, please consider (★) starring it (on top right of the page) so that it can reach a broader audience.