Neural Networks

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This chapter is not available online. It is only available from either the paperback or ebook version. To purchase this book, click here.

    Following is an example of a very simple neural network. Though the network is simple, it includes nearly all of the elements of the more complex neural networks that will be covered later in this book.

    First, consider an artificial neuron, as shown in Figure 1.6.

Figure 1.6: Artificial neuron.

    A significant goal of this book is to show you how to construct neural networks and to teach you when to use them. As a programmer of neural networks, you must understand which problems are well suited for neural network solutions and which are not. An effective neural network programmer also knows which neural network structure, if any, is most applicable to a given problem. This section begins by first focusing on those problems that are not conducive to a neural network solution.

• Understanding Biological Neural Networks
• How an Artificial Neural Network is Constructed
• Appropriate Uses for Neural Networks

Introduction to Neural Networks with C#, Second Edition, introduces the C# 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.

    Following is an example of a very simple neural network. Though the network is simple, it includes nearly all of the elements of the more complex neural networks that will be covered later in this book.

    First, consider an artificial neuron, as shown in Figure 1.6.

Figure 1.6: Artificial neuron.

    A significant goal of this book is to show you how to construct neural networks and to teach you when to use them. As a programmer of neural networks, you must understand which problems are well suited for neural network solutions and which are not. An effective neural network programmer also knows which neural network structure, if any, is most applicable to a given problem. This section begins by first focusing on those problems that are not conducive to a neural network solution.