Artificial Neural Networks with Java: Tools for Building Neural Network Applications

Artificial Neural Networks with Java: Tools for Building Neural Network Applications
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Book Description
Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you will manually process the first neural network example. This covers the internals of front and back propagation, and facilitates the understanding of the main principles of neural network processing. Artificial Neural Networks with Java also teaches you how to prepare the data to be used in neural network development and suggests various techniques of data preparation for many unconventional tasks. 
The next big topic discussed in the book is using Java for neural network processing. You will use the Encog Java framework and discover how to do rapid development with Encog, allowing you to create large-scale neural network applications.

The book also discusses the inability of neural networks to approximate complex non-continuous functions, and it introduces the micro-batch method that solves this issue. The step-by-step approach includes plenty of examples, diagrams, and screen shots to help you grasp the concepts quickly and easily.

What You Will Learn
  • Prepare your data for many different tasks
  • Carry out some unusual neural network tasks
  • Create neural network to process non-continuous functions
  • Select and improve the development model  
Who This Book Is For
Intermediate machine learning and deep learning developers who are interested in switching to Java.

Content

Chapter 1: Learning About Neural Networks
Chapter 2: Internal Mechanics of Neural Network Processing
Chapter 3: Manual Neural Network Processing
Chapter 4: Configuring Your Development Environment
Chapter 5: Neural Network Development Using the Java Encog Framework
Chapter 6: Neural Network Prediction Outside the Training Range
Chapter 7: Processing Complex Periodic Functions
Chapter 8: Approximating Noncontinuous Functions
Chapter 9: Approximating Continuous Functions with Complex Topology
Chapter 10: Using Neural Networks to Classify Objects
Chapter 11: The Importance of Selecting the Correct Model
Chapter 12: Approximation of Functions in 3D Space

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