Deep Learning Books

There are not many books on deep learning at the moment because it is such a young area of study.

There are a few books available though and some very interesting books in the pipeline that you can purchase by early access.

In this post, you will discover the books available right now on deep learning.

Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

Deep Learning Books (I wish)
Photo by Indi Samarajiva, some rights reserved.

Deep Learning Textbook

There is a deep learning textbook that has been under development for a few years called simply Deep Learning.

It is being written by top deep learning scientists Ian Goodfellow, Yoshua Bengio and Aaron Courville and includes coverage of all of the main algorithms in the field and even some exercises.

I think it will become the staple text to read in the field, primarily because they are giving it away for free (much like The Elements of Statistical Learning is required reading in Machine Learning).

A lot of it is complete already and I highly recommend reading it to get some background theory on deep learning algorithms.

Deep Learning

The current working table of contents looks as follows:

• Part I: Applied Math and Machine Learning Basics
  • Linear Algebra
  • Probability and Information Theory
  • Numerical Computation
  • Machine Learning Basics
• Part II: Modern Practical Deep Networks
  • Deep Feedforward Networks
  • Regularization
  • Optimization for Training Deep Models
  • Convolutional Networks
  • Sequence Modeling: Recurrent and Recursive Nets
  • Practical Methodology
  • Applications
• Part III: Deep Learning Research
  • Linear Factor Models
  • Autoencoders
  • Representation Learning
  • Structured Probabilistic Models for Deep Learning
  • Monte Carlo Methods
  • Confronting the Partition Function
  • Approximate Inference
  • Deep Generative Models

Deep Learning Books from O’Reilly

There are currently two books from O’Reilly that are in the pipeline that I am excited about:

• Deep Learning: A Practitioner’s Approach
• Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

Deep Learning: A Practitioner’s Approach

This is an applied book written by two of the creators of DeepLearning4J: Adam Gibson and Josh Patterson. DeepLearning4J (or DL4J) is the Deep Learning framework for Java applications.

The book is practical, written for both Java developers and data scientists and I can only assume it provides examples using the DL4J framework.

The book is due out in May 2016 and there is currently no table of contents available (that I could find).

Deep Learning- A Practitioner’s Approach

Fundamentals of Deep Learning

This is another applied book in Python written by Nikhil Buduma. It covers both deep learning concepts and examples. You can get early access to this book and there are 5 chapters available at the time of writing.

The currently working table of contents for this book is listed below:

• The Neural Network
• Training Feed Forward Neural Networks
• Implementing Neural Networks in TensorFlow
• Beyond Gradient Descent
• Convolutional Neural Networks:
• Embedding and Representation Learning
• Deep Learning Models for Sequence Analysis
• Memory-Augmented Deep Learning Models
• Generative Deep Learning Models
• Deep Reinforcement Learning
• Towards General Unsupervised Learning
• Training Extremely Deep Neural Networks

I’m excited to get my hands on this book.

Fundamentals of Deep Learning- Designing Next-Generation Machine Intelligence Algorithms

Deep Learning from Packt Publishing

Packet publishing has a book coming out in May 2016 on Deep Learning written by Yusuke Sugomori titled Deep Learning with Java. It is also targeting Java Developers and Data Scientists and will provide examples using the DeepLearning4J framework.

There does not appear to be a table of contents available for this book yet, but I believe you can get early access to it.

Deep Learning with Java

Deep Belief Nets in C++ and CUDA C

Timothy Masters has written a number of books on artificial neural networks over the years. In 2015 he wrote two books on Deep Belief Networks using C++ and CUDA.

The books provide examples and are primarily focused on his software called DEEP. You can learn more about his software on his webpage.

The two books are:

Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward Networks

The table of contents for this book is:

• Supervised Feedforward Networks
• Restricted Boltzmann Machines
• Greedy Training
• DEEP Operating Manual

Deep Belief Nets in C++ and CUDA C: Volume II: Autoencoding in the Complex Domain

The table of contents for this book is:

• Embedded Class Labels
• Signal Preprocessing
• Image Preprocessing
• Autoencoding
• DEEP Operating Manual

Artificial Intelligence for Humans

Jeff Heaton is a researcher and author of a series of three books on artificial intelligence:

• Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms
• Artificial Intelligence for Humans, Volume 2: Nature-Inspired Algorithms
• Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks

The third book in the series covers artificial neural networks and has a few chapters on deep learning techniques.

Artificial Intelligence for Humans

The table of contents looks as follows:

• Neural Network Basics
• Self-Organizing Maps
• Hopfield and Boltzmann Machines
• Feedforward Neural Networks
• Training and Evaluation
• Backpropagation Training
• Other Propagation Training
• NEAT, CPNN and HyperNEAT
• Deep Learning
• Convolutional Neural Networks
• Pruning and Model Selection
• Dropout and Regularization
• Time Series and Recurrent Networks
• Architecting Neural Networks
• Visualization
• Modeling with Neural Networks

Generally, Jeff is a good communicator and his books get attention from the community because he used KickStarter in the process to create them. The third part of his series might be a good read if you are looking for an introduction to neural networks and discover where deep learning fits in.

Deep Learning in R

N. D. Lewis has a series of books on statistics and machine learning including books on neural networks. In early 2016 he released a book on Deep Learning in R titled: Deep Learning Made Easy with R: A Gentle Introduction for Data Science.

Deep Learning in R

The table of contents looks as follows:

• Deep Neural Networks
• Elman Neural Networks
• Jordan Neural Networks
• The Secret to the Autoencoder
• The Stacked Autoencoder in a Nutshell
• Restricted Boltzmann Machines
• Deep Belief Networks

Update: More Books

This section lists addition books that have been released (or are expected to be released) after this post was written.

• Neural Networks and Deep Learning
• Grokking Deep Learning
• Machine Learning with TensorFlow
• TensorFlow Machine Learning Cookbook
• Getting Started with TensorFlow
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems

Summary

In this post you discover the books that are available right now on deep learning.

Have you purchased or read one of these books? Leave a comment and let me know what you think of it.

Are there any more books on deep learning that you know are coming or already here? Let me know in the comments.