¡°Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. It provides much-needed broad perspective and mathematical preliminaries for software engineers and students entering the field, and serves as a reference for authorities.¡±
-Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
¡°This is the definitive textbook on deep learning. Written by major contributors to the field, it is clear, comprehensive, and authoritative. If you want to know where deep learning came from, what it is good for, and where it is going, read this book.¡±
-Geoffrey Hinton FRS, Emeritus Professor, University of Toronto; Distinguished Research Scientist, Google
¡°Deep learning has taken the world of technology by storm since the beginning of the decade. There was a need for a textbook for students, practitioners, and instructors that includes basic concepts, practical aspects, and advanced research topics. This is the first comprehensive textbook on the subject, written by some of the most innovative and prolific researchers in the field. This will be a reference for years to come.¡±
-Yann LeCun, Director of AI Research, Facebook; Silver Professor of Computer Science, Data Science, and Neuroscience, New York University
1 Introduction
¥°Applied Math and Machine Learning Basics
2 Linear Algebra
3 Probability and Information Theory
4 Numerical Computation
5 Machine Learning Basics
¥±Deep Networks: Modern Practices
6 Deep Feedfoward Networks
7 Regularization for Deep Leaning
8 Optimization for Raining Deep Models
9 Convolutional Networks
10 Sequence Modeling: Recurrent and Recursive Nets
11 Practical Methodology
12 Applications
¥² Deep Leaning Research
13 Linear Factor Models
14 Autoencoders
15 Representation Learning
16 Structured Probabilistic Models for Deep Leaning
17 Monte Carlo Methods
18 Confonting the Partition Function
19 Approximate Inference
20 Deep Generative Models