My reading notes of the book Deep Learning by Goodfellow et al.

Chaper 1 - Introduction

PART I
Chapter 2 - Linear Algebra
Chapter 3 - Probability and Information Theory
Chapter 4 - Numerical Computation
Chapter 5 - Machine Learning Basics

PART II
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

PART III
Chapter 13 - Linear Factor Models
Chapter 14 - Autoencoders
Chapter 15 - Representation Learning
Chapter 16 - Structured Probabilistic Models for Deep Learning
Chapter 17 - Monte Carlo Methods
Chapter 18 - Confronting the Partition Function
Chapter 19 - Approximate Inference
Chapter 20 - Deep Generative Models