Deep Learning

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” — Elon Musk, co-chair of OpenAI; co-founder and CEO of Tesla and SpaceX

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Table of Contents

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
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: Deep Learning Research
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