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>

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Table of Contents (19 chapters)
Neural Network Programming with Java Second Edition
Neural Network Programming with Java Second Edition
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Getting Started with Neural Networks
Getting Started with Neural Networks
Discovering neural networks
Why artificial neural networks?
From ignorance to knowledge – learning process
Let the coding begin! Neural networks in practice
The neuron class
The NeuralLayer class
The ActivationFunction interface
The neural network class
Time to play!
Summary
2
Getting Neural Networks to Learn
Getting Neural Networks to Learn
Learning ability in neural networks
Learning paradigms
The learning process
Examples of learning algorithms
Time to see the learning in practice!
Amazing, it learned! Or, did it really? A further step – testing
Summary
3
Perceptrons and Supervised Learning
Perceptrons and Supervised Learning
Supervised learning – teaching the neural net
A basic neural architecture – perceptrons
Multi-layer perceptrons
Learning in MLPs
Practical example 1 – the XOR case with delta rule and backpropagation
Practical example 2 – predicting enrolment status
Summary
4
Self-Organizing Maps
Self-Organizing Maps
Neural networks unsupervised learning
Unsupervised learning algorithms
Kohonen self-organizing maps
Summary
5
Forecasting Weather
Forecasting Weather
Neural networks for regression problems
Loading/selecting data
Choosing input and output variables
Preprocessing
Empirical design of neural networks
Summary
6
Classifying Disease Diagnosis
Classifying Disease Diagnosis
Foundations of classification problems
Logistic regression
Neural networks for classification
Disease diagnosis with neural networks
Summary
7
Clustering Customer Profiles
Clustering Customer Profiles
Clustering tasks
Applied unsupervised learning
Profiling
Summary
8
Text Recognition
Text Recognition
Pattern recognition
Neural networks in pattern recognition
Summary
9
Optimizing and Adapting Neural Networks
Optimizing and Adapting Neural Networks
Common issues in neural network implementations
Input selection
Online retraining
Adaptive neural networks
Summary
10
Current Trends in Neural Networks
Current Trends in Neural Networks
Deep learning
Deep architectures
Implementing a hybrid neural network
Summary
References
References
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
Index
Index

Kohonen self-organizing maps

This network architecture was created by the Finnish professor Teuvo Kohonen at the beginning of the 80s. It consists of one single layer neural network capable of providing a visualization of the data in one or two dimensions.

In this book, we are going to use Kohonen networks also as a basic competitive layer with no links between the neurons. In this case, we are going to consider it as zero dimension (0-D).

Theoretically, a Kohonen Network would be able to provide a 3-D (or even in more dimensions) representation of the data; however, in printed material such as this book, it is not practicable to show 3-D charts without overlapping some data. Thus in this book, we are going to deal only with 0-D, 1-D, and 2-D Kohonen networks.

Kohonen Self-Organizing Maps (SOMs), in addition to the traditional single layer competitive neural networks (in this book, the 0-D Kohonen network), add the concept of neighborhood neurons. A dimensional SOM takes into account the index...

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