Neural Network Programming
Neural Network Programming With Java

Neural Network Programming with Java Book Description:

Vast quantities of data are produced every second. In this context, neural networks become a powerful technique to extract useful knowledge from large amounts of raw, seemingly unrelated data. One of the most preferred languages for neural network programming is Java as it is easier to write code using it, and most of the most popular neural network packages around already exist for Java. This makes it a versatile programming language for neural networks.

This book gives you a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java.

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 the concepts you’ve learned. Furthermore, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, 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.

What You Will Learn

  • Get to grips with the basics of neural networks and what they are used for
  • Develop neural networks using hands-on examples
  • Explore and code the most widely-used learning algorithms to make your neural network learn from most types of data
  • 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
  • Discover how to improve and optimize your neural network

Table of Contents

1: GETTING STARTED WITH NEURAL NETWORKS
2: HOW NEURAL NETWORKS LEARN
3: HANDLING PERCEPTRONS
4: SELF-ORGANIZING MAPS
5: FORECASTING WEATHER
6: CLASSIFYING DISEASE DIAGNOSIS
7: CLUSTERING CUSTOMER PROFILES
8: PATTERN RECOGNITION (OCR CASE)
9: NEURAL NETWORK OPTIMIZATION AND ADAPTATION

 


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