Book Image

Neural Network Programming with Java - Second Edition

By : Fabio M. Soares, Alan M. F. Souza
Book Image

Neural Network Programming with Java - Second Edition

By: Fabio M. Soares, Alan M. F. Souza

Overview of this book

<p>Want to discover the current state-of-art in the field of neural networks that will let you understand and design new strategies to apply to more complex problems? This book takes you on a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java, giving you everything you need to stand out.</p> <p>You will first learn the basics of neural networks and their process of learning. We then focus on what Perceptrons are and their features. Next, you will implement self-organizing maps using practical examples. Further on, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning, and characters recognition (OCR). Finally, you will learn methods to optimize and adapt neural networks in real time.</p> <p>All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience.</p>
Table of Contents (19 chapters)
Neural Network Programming with Java Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Summary


In this chapter, we've seen an introduction to the neural networks, what they are, what they are used for, and their basic concepts. We've also seen a very basic implementation of a neural network in the Java programming language, wherein we applied the theoretical neural network concepts in practice, by coding each of the neural network elements. It's important to understand the basic concepts before we move on to advanced concepts. The same applies to the code implemented with Java.

In the next chapter, we will delve into the learning process of a neural network and explore the different types of leaning with simple examples.