Exploring Neural Networks with C#

The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations—making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical.

Exploring Neural Networks with C# presents the important properties of neural networks—while keeping the complex mathematics to a minimum. Explaining how to build and use neural networks, it presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand.

Taking a “learn by doing” approach, the book is filled with illustrations to guide you through the mystery of neural networks. Examples of experiments are provided in the text to encourage individual research. Online access to C# programs is also provided to help you discover the properties of neural networks.

Following the procedures and using the programs included with the book will allow you to learn how to work with neural networks and evaluate your progress. You can download the programs as both executable applications and C# source code from http://home.agh.edu.pl/~tad//index.php?page=programy&lang=en

Table of Contents

Chapter 1: Introduction to Natural and Artificial Neural Networks
Chapter 2: Neural Net Structure
Chapter 3: Teaching Networks
Chapter 4: Functioning of Simplest Networks
Chapter 5: Teaching Simple Linear One-Layer Neural Networks
Chapter 6: Nonlinear Networks
Chapter 7: Backpropagation
Chapter 8: Forms of Neural Network Learning
Chapter 9: Self-Learning Neural Networks
Chapter 10: Self-Organizing Neural Networks
Chapter 11: Recurrent Networks