What is a neural network (NN)?
First of all, when we are talking about a neural network, we should more properly say “artificial neural network” (ANN), because that is what we mean most of the time in comp.ai.neural-nets. Biological neural networks are much more complicated than the mathematical models we use for ANNs. But it is customary to be lazy and drop the “A” or the “artificial”. There is no universally accepted definition of an NN. But perhaps most people in the field would agree that an NN is a network of many simple processors (“units”), each possibly having a small amount of local memory. The units are connected by communication channels (“connections”) which usually carry numeric (as opposed to symbolic) data, encoded by any of various means. The units operate only on their local data and on the inputs they receive via the connections. The restriction to local operations is often relaxed during training. Some NNs are models of biological neural networks and some are not, but historically, much of the ins
