Apologies if this is slightly offtopic, but is there a high-quality Python
implementation of DropOut / DropConnect available somewhere?
On Wed, Feb 5, 2014 at 12:58 PM, Andy <t3k...@gmail.com> wrote:
> 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
>
>
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