Belated thanks, that's extremely helpful. I'll take a look at those links which are unfamiliar to me (I've already read the Nature of Code link, it's a great b

Belated thanks, that's extremely helpful. I'll take a look at those links which are unfamiliar to me (I've already read the Nature of Code link, it's a great book.) Tidbits: + Yes, the code presented "works" for multiple hidden layers. But it converges too slowly to be useful, except for very small networks. Later chapters introduce new ideas which help backprop converge faster, and which start to make multiple hidden layers quite feasible. + On the broader machine learning front, I think scikit learn is a pretty nice general library: it's approachable, easy to use, and has reasonable docs. It'll be interesting to see how it develops. + For neural nets in particular, elsewhere in this thread Dave Sullivan has done a nice job distilling out a list of good libraries. I think it'll be a while before there's a real out-of-the-box solution for neural nets, though, since setting parameters for neural nets is something of an art.