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.

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

• Aman Dalmia
• Ameya Godbole

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

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