Introduction to Neural Networks for C#, 2nd Edition by Jeff Heaton

By Jeff Heaton

Creation to Neural Networks with C#, moment version, introduces the C# programmer to the area of Neural Networks and synthetic Intelligence. Neural community architectures, equivalent to the feedforward, Hopfield, and self-organizing map architectures are mentioned. education innovations, similar to backpropagation, genetic algorithms and simulated annealing also are brought. useful examples are given for every neural community. Examples comprise the touring salesman challenge, handwriting reputation, monetary prediction, video game process, mathematical services, and net bots. All C# resource code is accessible on-line for simple downloading.

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1: A typical matrix. 398 XXXIII XXXIV Introduction to Neural Networks for C#, Second Edition Introduction Introduction This book provides an introduction to neural network programming using C#. It focuses on the feedforward neural network, but also covers Hopfield neural networks, as well as self-organizing maps. 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.

Pattern recognition is a form of classification. Pattern recognition is simply the ability to recognize a pattern. The pattern must be recognized even when it is distorted. Consider the following everyday use of pattern recognition. Every person who holds a driver’s license should be able to accurately identify a traffic light. This is an extremely critical pattern recognition procedure carried out by countless drivers every day. However, not every traffic light looks the same, and the appearance of a particular traffic light can be altered depending on the time of day or the season.

Further, since many of the same mathematical operations performed on the weight matrix are also performed on the threshold values, having them contained in a single matrix allows these operations to be performed more efficiently. Matrix Classes This chapter presents several classes that can be used to create and manipulate matrixes. These matrix classes will be used throughout this book. 1. 1: Matrix Classes Class Purpose BiPolarUtil A utility class to convert between Boolean and bipolar numbers.

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