On 2012-06-05, at 1:51 PM, David Marek <h4wk...@gmail.com> wrote: > 1) Afaik all you need is one hidden layer,
The universal approximator theorem says that any continuous function can be approximated arbitrarily well if you have one hidden layer with enough hidden units, but it says nothing about the ease of finding that solution, nor about the efficiency of the solution (you can prove that certain functions that can be compactly represented by a deep network require exponentially many more hidden units if you're restricted to one layer). However, with purely supervised training deeper networks are harder to fit (you can get to about 2 hidden layers if you're careful but beyond that it gets quite hard), so I wouldn't worry about it. In a "black box" implementation for scikit-learn, where the user isn't expected to be an expert in training neural nets, a single hidden layer is probably plenty. David ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
