Artificial Neural Networks with Java

  • 4h 25m
  • Igor Livshin
  • Apress
  • 2021

Develop neural network applications using the Java environment. After learning the rules involved in neural network processing, this second edition shows you how to manually process your first neural network example.

The book covers the internals of front and back propagation and helps you understand the main principles of neural network processing. You also will learn how to prepare the data to be used in neural network development and you will be able to suggest various techniques of data preparation for many unconventional tasks.

This book discusses the practical aspects of using Java for neural network processing. You will know how to use the Encog Java framework for processing large-scale neural network applications. Also covered is the use of neural networks for approximation of non-continuous functions. In addition to using neural networks for regression, this second edition shows you how to use neural networks for computer vision. It focuses on image recognition such as the classification of handwritten digits, input data preparation and conversion, and building the conversion program. And you will learn about topics related to the classification of handwritten digits such as network architecture, program code, programming logic, and execution.

The step-by-step approach taken in the book includes plenty of examples, diagrams, and screenshots to help you grasp the concepts quickly and easily.

What You Will Learn

  • Use Java for the development of neural network applications
  • Prepare data for many different tasks
  • Carry out some unusual neural network processing
  • Use a neural network to process non-continuous functions
  • Develop a program that recognizes handwritten digits

In this Book

  • Learning About Neural Networks
  • Internal Mechanics of Neural Network Processing
  • Manual Neural Network Processing
  • Configuring Your Development Environment
  • Neural Networks Development Using the Java Encog Framework
  • Neural Network Prediction Outside of the Training Range
  • Processing Complex Periodic Functions
  • Approximating Noncontinuous Functions
  • Approximation of Continuous Functions with Complex Topology
  • Using Neural Networks for the Classification of Objects
  • The Importance of Selecting the Correct Model
  • Approximation Functions in 3D Space
  • Image Recognition
  • Classification of Handwritten Digits


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