Deep Learning Book Notes

Hi there, these are a collection of notes for the Deep Learning Book.

These are still under construction, but if you see an error or would like something added, please feel free to send us a PR!

Chapter 1: Introduction

Part I: Applied Math and Machine Learning Basics

Chapter 2: Linear Algebra

Chapter 3: Probability and Information Theory

Chapter 4: Numerical Computation

Chapter 5: Machine Learning Basics

Part II: Modern Practical Deep Networks

Chapter 6: Deep Feedforward Networks

Chapter 7: Regularization for Deep Learning

Chapter 8: Optimization for Deep Learning

Part III: Deep Learning Research

Chapter 14: Autoencoders