On 2012-05-16, at 6:31 AM, Andreas Mueller <[email protected]> wrote:
> Btw, I am not sure theano is the best way to compute derivatives ;) No? I would agree in the general case. However, in the case of MLPs and backprop, it's a use case for which Theano has been designed and heavily optimized. With it, it's very easy and quick to produce a correct MLP implementation (the deep learning tutorials contain one). It's *not* the best way to obtain a readable mathematical expression for the gradients, but it'll allow you to compute them easily/correctly, which makes it a useful thing to verify against. I've done this a fair bit myself. I've never had so much success with symbolic tools like Wolfram Alpha in situations involving lots of sums over indexed scalar quantities and whatnot, but perhaps I didn't try hard enough. Once the initial version is working, Theano will serve another purpose: as a speed benchmark to try and beat (or at least not be too far behind). :) 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 [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
