This paper considers the feed-forward training problem from the numerical point of view, in particular the conditioning of the problem. It is well known that the feed-forward training problem is often ill-conditioned; this affects the behaviour of training algorithms, the choice of such algorithms and the quality of the solutions achieved. A geometric interpretation of ill-conditioning is explored and an example of function approximation is analysed in detail.
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