Deep Learning

Preface

What’s in This Book?

The first four chapters of this book are focused on enough theory and fundamentals to give you, the practitioner, a working foundation for the rest of the book. The last five chapters then work from these concepts to lead you through a series of practical paths in deep learning using DL4J:

• Building deep networks
• Advanced tuning techniques
• Vectorization for different data types
• Running deep learning workflows on Spark

DL4J as Shorthand for Deeplearning4j

We use the names DL4J and Deeplearning4j interchangeably in this book. Both terms refer to the suite of tools in the Deeplearning4j library.

We designed the book in this manner because we felt there was a need for a book covering “enough theory” while being practical enough to build production-class deep learning workflows. We feel that this hybrid approach to the book’s coverage fits this space well.

Chapter 1 is a review of machine learning concepts in general as well as deep learning in particular, to bring any reader up to speed on the basics needed to understand the rest of the book. We added this chapter because many beginners can use a refresher or primer on these concepts and we wanted to make the project accessible to the largest audience possible.

Chapter 2 builds on the concepts from Chapter 1 and gives you the foundations of neural networks. It is largely a chapter in neural network theory but we aim to present the information in an accessible way. Chapter 3 further builds on the first ...