Neural Network Programming with Java, 2nd Edition

Create and unleash the power of neural networks by implementing professional Java code

About This Book

• Learn to build amazing projects using neural networks including forecasting the weather and pattern recognition
• Explore the Java multi-platform feature to run your personal neural networks everywhere
• This step-by-step guide will help you solve real-world problems and links neural network theory to their application

Who This Book Is For

This book is for Java developers who want to know how to develop smarter applications using the power of neural networks. Those who deal with a lot of complex data and want to use it efficiently in their day-to-day apps will find this book quite useful. Some basic experience with statistical computations is expected.

What You Will Learn

• Develop an understanding of neural networks and how they can be fitted
• Explore the learning process of neural networks
• Build neural network applications with Java using hands-on examples
• Discover the power of neural network’s unsupervised learning process to extract the intrinsic knowledge hidden behind the data
• Apply the code generated in practical examples, including weather forecasting and pattern recognition
• Understand how to make the best choice of learning parameters to ensure you have a more effective application
• Select and split data sets into training, test, and validation, and explore validation strategies

In Detail

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.

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.

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.

Style and approach

This book takes you on a steady learning curve, teaching you the important concepts while being rich in examples. You’ll be able to relate to the examples in the book while implementing neural networks in your day-to-day applications.

Table of Contents

Chapter 1. Getting Started with Neural Networks
Chapter 2. Getting Neural Networks to Learn
Chapter 3. Perceptrons and Supervised Learning
Chapter 4. Self-Organizing Maps
Chapter 5. Forecasting Weather
Chapter 6. Classifying Disease Diagnosis
Chapter 7. Clustering Customer Profiles
Chapter 8. Text Recognition
Chapter 9. Optimizing and Adapting Neural Networks
Chapter 10. Current Trends in Neural Networks