Issam Laradji implemented a multilayer perceptron and extreme learning
machines for last year's GSoC. Both are awaiting final reviews before being
merged. They should be functional and can be found in the Issue Tracker.
On 7 April 2015 at 21:09, Vlad Ionescu <ionescu.vl...@gmail.com> wrote:
> Hello,
>
> I was wondering why there isn't a classic neural network implementation in
> scikit-learn (a multilayer perceptron). This could have varying levels of
> complexity: it could be hardcoded to just one hidden layer, allowing one to
> specify the type of neurons in it (sigmoid, tanh, rectified linear etc.),
> the learning rate and values for weight decay and momentum.
>
> It could also be made to accept multiple hidden layers, with the ability
> to specify the number of neurons and their type for each one.
>
> Has this been considered before but no one has gotten around to it? Would
> it be of interest for you?
>
> There are of course more sophisticated methods that would be nice to have
> as well. I'm only asking about the basic type because that is what I
> currently would be willing to help with, but it would be great if more were
> under consideration.
>
>
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