A practical overview of neural networks

This paper overviews the myths and misconceptions that have surrounded neural networks in recent years. Focusing on backpropagation and the Hopfield network, we discuss the problems that have plagued practical application of these techniques, and review some of the recent progress made. Both real and perceived inadequacies of backpropagation are discussed, as well as the need for an understanding of statistics and of the problem domain in order to apply and assess the neural network properly. We consider alternatives or variants to backpropagation, which overcome some of its real limitations. The Hopfield network's poor performance on the traveling salesman problem in combinatorial optimization has colored its reception by engineers; we describe both new research in this area and promising results in other practical optimization applications. Overall, it is hoped, this paper will aid in a more balanced understanding of neural networks. They seem worthy of consideration in many applications, but they do not deserve the status of a panacea – nor are they as fraught with problems as would now seem to be implied.