Include index.

A review of machine learning
Foundations of neural networks and deep learning
Fundamentals of deep networks
Major architecture of deep networks
Building deep networks
Tuning deep networks
Tuning specific deep network architectures
Vectorization
Using deep learning and DL4J on Spark
What is artificial intelligence?
RL4J and reinforcement learning
Numbers everyone should know
Neural networks and backpropagation: a mathematical approach
Using the ND4J API
Using DataVec
Working with DL4J from source
Setting up DL4J projects
Setting up GPUs for DL4J projects
Troubleshooting DL4J installations

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.

Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.

Dive into machine learning concepts in general, as well as deep learning in particular
Understand how deep networks evolved from neural network fundamentals
Explore the major deep network architectures, including Convolutional and Recurrent
Learn how to map specific deep networks to the right problem
Walk through the fundamentals of tuning general neural networks and specific deep network architectures
Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool
Learn how to use DL4J natively on Spark and Hadoop