Introduction to Neural Networks with Java, 2nd Edition
by Jeff Heaton
ISBN: 1-60439-008-5
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This archive contains the Java source code from the book "Introduction to 
Neural Networks with Java". If you would like to purchase this book you may
do so at the following URL:

http://www.heatonresearch.com/book/

You can also view much of the book online, at the following URL:

http://www.heatonresearch.com/articles/series/1/

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Table of Contents from "Introduction to Neural Networks with Java"

Introduction to Neural Networks with Java, Second Edition, introduces the Java programmer 
to the world of Neural Networks and Artificial Intelligence. Neural network architectures, 
such as the feedforward, Hopfield, and self-organizing map architectures are discussed. 
Training techniques, such as backpropagation, genetic algorithms and simulated annealing 
are also introduced. Practical examples are given for each neural network. Examples 
include the traveling salesman problem, handwriting recognition, financial prediction, 
game strategy, mathematical functions, and Internet bots. All Java source code is available 
online for easy downloading. 

Chapter 1 provides an overview of neural networks. You will be introduced to the mathematical 
underpinnings of neural networks and how to calculate their values manually. You will also 
see how neural networks use weights and thresholds to determine their output. Matrix math plays 
a central role in neural network processing. 

Chapter 2 introduces matrix operations and demonstrates how to implement them in Java. The 
mathematical concepts of matrix operations used later in this book are discussed. Additionally, 
Java classes are provided which accomplish each of the required matrix operations. One of the 
most basic neural networks is the Hopfield neural network. 

Chapter 3 demonstrates how to use a Hopfield Neural Network. You will be shown how to 
construct a Hopfield neural network and how to train it to recognize patterns. 

Chapter 4 introduces the concept of machine learning. To train a neural network, the weights 
and thresholds are adjusted until the network produces the desired output. There are many 
different ways training can be accomplished. This chapter introduces the different training 
methods. 

Chapter 5 introduces perhaps the most common neural network architecture, the feedforward 
backpropagation neural network. This type of neural network is the central focus of this book. 
In this chapter, you will see how to construct a feedforward neural network and how to train 
it using backpropagation. Backpropagation may not always be the optimal training algorithm. 

Chapter 6 expands upon backpropagation by showing how to train a network using a genetic 
algorithm. A genetic algorithm creates a population of neural networks and only allows the 
best networks to ÒmateÓ and produce offspring. Simulated annealing can also be a very 
effective means of training a feedforward neural network. 

Chapter 7 continues the discussion of training methods by introducing simulated annealing. 
Simulated annealing simulates the heating and cooling of a metal to produce an optimal solution. 
Neural networks may contain unnecessary neurons. 

Chapter 8 explains how to prune a neural network to its optimal size. Pruning allows unnecessary 
neurons to be removed from the neural network without adversely affecting the error 
rate of the network. The neural network will process information more quickly with fewer 
neurons. Prediction is another popular use for neural networks. 

Chapter 9 introduces temporal neural networks, which attempt to predict the future. Prediction 
networks can be applied to many different problems, such as the prediction of sunspot cycles, 
weather, and the financial markets. 

Chapter 10 builds upon chapter 9 by demonstrating how to apply temporal neural networks to 
the financial markets. The resulting neural network attempts to predict the direction of 
the S & P 500. Another neural network architecture is the self-organizing map (SOM). SOMÕs 
are often used to group input into categories and are generally trained with an unsupervised 
training algorithm. An SOM uses a winner-takes-all strategy, in which the output is provided 
by the winning neuronÑoutput is not produced by each of the neurons. 

Chapter 11 provides an introduction to SOMs and demonstrates how to use them.
Handwriting recognition is a popular use for SOMs. 

Chapter 12 continues where  chapter 11 leaves off, by demonstrating how to use an SOM to 
read handwritten characters. The neural network must be provided with a sample of the handwriting 
that it is to analyze. This handwriting is categorized using the 26 characters of the 
Latin alphabet. The neural network is then able to recognize new characters. 

Chapter 13 introduces bot programming and explains how to use a neural network to help 
identify data. Bots are computer programs that perform repetitive tasks. An HTTP bot is a 
special type of bot that uses the web much like a human uses it. The neural network is 
trained to recognize the specific types of data for which the bot is searching. 

The book ends with chapter 14, which discusses the future of neural networks, quantum 
computing, and how it applies to neural networks. The Encog neural network framework is 
also introduced.