On 02/05/2014 04:30 PM, Gael Varoquaux wrote: > On Wed, Feb 05, 2014 at 03:02:24PM +0300, Issam wrote: >> I have been working with scikit-learn for three pull requests - namely, >> Multi-layer Perceptron (MLP), Sparse Auto-encoders, and Gaussian >> Restricted Boltzmann Machines. > Yes, you have been doing good work here! +1 >> For the upcoming GSoC, I propose to ensure completing these three pull >> requests. I also would develop Greedy layer-wise training algorithm for >> deep learning, extending MLP to allow for more than one hidden layer, >> where weights are initialized using Sparse Auto-encoders or RBM. >> How will this suit for GSoC? > The MLP is almost finished. I would hope that it would be finished before > the GSoC. Actually, I was hoping that it could be finished before next > release. I'm also still hopeful there. Unfortunately I will definitely be unable to mentor.
About pretraining: that is really out of style now ;) Afaik "everybody" is now doing purely supervised training using drop-out. Implementing pretrained deep nets should be fairly easy for a user if we support more than one hidden layer, but just doing a pipeline of RBMs / Autoencoders. As that is not that popular any more, I don't think we should put much effort there. Deeper nets might be interesting but I'm quite sceptical about doing without GPUs. On the other hand I think it should be possible for you to find a topic around these general concepts. But I'm not sure who could mentor. Cheers, Andy ------------------------------------------------------------------------------ Managing the Performance of Cloud-Based Applications Take advantage of what the Cloud has to offer - Avoid Common Pitfalls. Read the Whitepaper. http://pubads.g.doubleclick.net/gampad/clk?id=121051231&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
