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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


  • 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]



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.


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