Chapter 1, Introduction.

Chapter 2, Linear Algebra.

Chapter 3, Probability and Information Theory.

Chapter 4, Numerical Computation.

Chapter 5, Machine Learning Basics.

Chapter 6, Deep Feedforward Networks.

Chapter 7, Regularization for Deep Learning.

Chapter 8, Optimization for Training Deep Models.

Chapter 9, Convolutional Networks.

Chapter 10, Sequence Modeling: Recurrent and Recursive Nets.

Chapter 11, Practical Methodology.

Chapter 12, Applications.

Chapter 15, Representation Learning.

Chapter 16, Structured Probabilistic Models for Deep Learning.

Chapter 18, Confronting the Partition Function.

Chapter 19, Approximate Inference.

Chapter 20, Deep Generative Models.

http://www.mediafire.com/file/4zdzf31o417llc0/Deep_Learning_Book_MIT.pdf

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