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.
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Part I: Applied Math and Machine Learning Basics
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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]
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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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