Abstract
This chapter discusses the way to implement a few examples of neural network tools (NNTs) on personal computers. The implementations are done step by step, with explanations along the way. Each NNT architecture (topology, model) has been selected because of its successful track record in solving practical problems on PC-based systems. The PC implementations presented in the chapter are not meant to represent generic versions of any model. They are merely representative samples of a few NNTs that the authors believe to be potentially useful to a wide range of users. The chapter describes the back-propagation model in terms of the architecture of the NNT that implements it. The term architecture, as applied to neural networks, has been used in different ways by different authors. Often, its meaning has been taken to be basically equivalent to topology, that is, the pattern of nodes and interconnections, together with such other items as directions of data flow and node-activation functions.
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