Deep-Learning-Book-Chapter-Summaries - Attempting to make the Deep Learning Book easier to understand.

Related tags

Deep Learning machine-learning deep-learning mathematics deep-learning-book
Overview

Deep-Learning-Book-Chapter-Summaries

This repository provides a summary for each chapter of the Deep Learning book by Ian Goodfellow, Yoshua Bengio and Aaron Courville and attempts to explain some of the concepts in greater detail. Some of the tougher chapters have blog post(s) dedicated to them which can be found on http://medium.com/inveterate-learner.

Chapters

  • Part I: Applied Math and Machine Learning Basics

    • Chapter 2: Linear Algebra [chapter]
    • Chapter 3: Probability and Information Theory [chapter]
    • Chapter 4: Numerical Computation [chapter]
    • Chapter 5: Machine Learning Basics [chapter]

  • Part II: Modern Practical Deep Networks

    • Chapter 6: Deep Feedforward Networks [chapter]
    • Chapter 7: Regularization for Deep Learning [chapter]
    • Chapter 8: Optimization for Training Deep Models [chapter]
    • Chapter 9: Convolutional Networks [chapter]
    • Chapter 10: Sequence Modeling: Recurrent and Recursive Nets [chapter]
    • Chapter 11: Practical Methodology [chapter]
    • Chapter 12: Applications [chapter]

  • Part III: Deep Learning Research

    • Chapter 13: Linear Factor Models [chapter]
    • Chapter 14: Autoencoders [chapter]
    • Chapter 15: Representation Learning [chapter]
    • Chapter 16: Structured Probabilistic Models for Deep Learning [chapter]
    • Chapter 17: Monte Carlo Methods [chapter]
    • Chapter 18: Confronting the Partition Function [chapter]
    • Chapter 19: Approximate Inference [chapter]
    • Chapter 20: Deep Generative Models [chapter]

Contributors

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.

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Comments
  • Chapter 2 Euclidean norm

    Chapter 2 Euclidean norm

    First, I really thank for your work, which does help me! I think you can promote it better by mentioning about squared L2 norm in front of x.T.dot(x) or sqrt(x.T.dot(x)) where may make beginners like me confused about Euclidean norm.

    opened by haofengsiji 0
  • Chapter 9 Suggestions

    Chapter 9 Suggestions

    Found this pretty useful. Nice effort 👍

    A few suggestions:

    • A very common ask (and slightly underrepresented in this chapter) has been receptive fields. Would do you like to include the same ?

    (Ref: https://arxiv.org/pdf/1603.07285.pdf)

    A few rendering issues:

    • Mathjax ($$ h(K,G,s){i,j,k} = \frac{\partial}{\partial V{i,j,k}}J(V,K) \ = \sum_{\substack{l,m \ s.t. \ (l-1)\times s+m=j}} \sum_{\substack{n,p \ s.t. \ (n-1)\times s+p=k}} \sum_{q} K_{q,i,m,p}G_{q,l,n} $$)

    • Could you clean up warnings on the scikit usage (code cell 1,3)?

    opened by varun19299 0
Owner
Aman Dalmia
Building Plio (https://plio.in) | Engineer, Lifelong Learner
Aman Dalmia
GitHub http://medium.com/inveterate-learner
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