PRACTICAL NETWORK DESIGN AND IMPLEMENTATION
The aim of this paper is to put Artificial Neural Network techniques in perspective, discussing the practical issues of network design and implementation. Firstly, a comparison is made with other pattern matching techniques with an overview of the capabilities and complexity of these techniques. Some basic ground in multi-layer perceptrons is then covered so that a link may be made between these and Gaussian classifiers. In the process, some of the practical difficulties associated with gradient descent based training are covered, along with the popular remedies. Finally, the paper is concluded with a summary of the advantages of recurrent networks and a review of the architectures available.
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