Neural Network Programming with Java

Neural networks are very intelligent algorithmic systems. Learn how to create them with Java with this guide dedicated to cutting-edge neural network development

Neural Network Programming with Java

Alan M. F. Souza, Fabio M. Soares

2 customer reviews
Neural networks are very intelligent algorithmic systems. Learn how to create them with Java with this guide dedicated to cutting-edge neural network development
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Book Details

ISBN 139781785880902
Paperback244 pages

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.

Table of Contents

Chapter 1: Getting Started with Neural Networks
Discovering neural networks
Why artificial neural network?
How neural networks are arranged
Learning about neural network architectures
From ignorance to knowledge – learning process
Let the implementations begin! Neural networks in practice
Summary
Chapter 2: How Neural Networks Learn
Learning ability in neural networks
Learning paradigms
Systematic structuring – learning algorithm
Examples of learning algorithms
Coding of the neural network learning
Two practical examples
Summary
Chapter 3: Handling Perceptrons
Studying the perceptron neural network
Popular multilayer perceptrons (MLPs)
Interesting MLP applications
Learning process in MLPs
Hands-on MLP implementation!
Levenberg–Marquardt implementation
Practical application – types of university enrolments
Summary
Chapter 4: Self-Organizing Maps
Neural networks' unsupervised way of learning
Some unsupervised learning algorithms
Kohonen self-organizing maps (SOMs)
Coding of the Kohonen algorithm
Summary
Chapter 5: Forecasting Weather
Neural networks for prediction problems
No data, no neural net – selecting data
Adjusting values – data preprocessing
Java implementation for weather prediction
Empirical design of neural networks
Summary
Chapter 6: Classifying Disease Diagnosis
What are classification problems, and how can neural networks be applied to them?
A special type of activation function – Logistic regression
Applying neural networks for classification
Disease diagnosis with neural networks
Summary
Chapter 7: Clustering Customer Profiles
Clustering task
Applied unsupervised learning
Customer profiling
Implementation in Java
Summary
Chapter 8: Pattern Recognition (OCR Case)
What is pattern recognition all about?
How to apply neural networks in pattern recognition
The OCR problem
Let the coding begin!
Summary
Chapter 9: Neural Network Optimization and Adaptation
Common issues in neural network implementations
Input selection
Structure selection
Online retraining
Adaptive neural networks
Summary

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

Authors

Table of Contents

Chapter 1: Getting Started with Neural Networks
Discovering neural networks
Why artificial neural network?
How neural networks are arranged
Learning about neural network architectures
From ignorance to knowledge – learning process
Let the implementations begin! Neural networks in practice
Summary
Chapter 2: How Neural Networks Learn
Learning ability in neural networks
Learning paradigms
Systematic structuring – learning algorithm
Examples of learning algorithms
Coding of the neural network learning
Two practical examples
Summary
Chapter 3: Handling Perceptrons
Studying the perceptron neural network
Popular multilayer perceptrons (MLPs)
Interesting MLP applications
Learning process in MLPs
Hands-on MLP implementation!
Levenberg–Marquardt implementation
Practical application – types of university enrolments
Summary
Chapter 4: Self-Organizing Maps
Neural networks' unsupervised way of learning
Some unsupervised learning algorithms
Kohonen self-organizing maps (SOMs)
Coding of the Kohonen algorithm
Summary
Chapter 5: Forecasting Weather
Neural networks for prediction problems
No data, no neural net – selecting data
Adjusting values – data preprocessing
Java implementation for weather prediction
Empirical design of neural networks
Summary
Chapter 6: Classifying Disease Diagnosis
What are classification problems, and how can neural networks be applied to them?
A special type of activation function – Logistic regression
Applying neural networks for classification
Disease diagnosis with neural networks
Summary
Chapter 7: Clustering Customer Profiles
Clustering task
Applied unsupervised learning
Customer profiling
Implementation in Java
Summary
Chapter 8: Pattern Recognition (OCR Case)
What is pattern recognition all about?
How to apply neural networks in pattern recognition
The OCR problem
Let the coding begin!
Summary
Chapter 9: Neural Network Optimization and Adaptation
Common issues in neural network implementations
Input selection
Structure selection
Online retraining
Adaptive neural networks
Summary

Book Details

ISBN 139781785880902
Paperback244 pages
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