neural nets are already well supported in other python libraries and don't
fit the current transformer model that scikit-learn uses
On Thu, May 7, 2015 at 12:55 AM, Sebastian Raschka <se.rasc...@gmail.com>
wrote:
> I am not one of the core developers, just a typical user, but although I
> think that neural nets would be a nice addition, I have to admit that I
> wouldn't count them as top priority. I think that in applications, neural
> networks require far more flexibility for tweaking than the "classic"
> off-the-shelve learning algorithms currently implemented in scikit-learn. I
> think that it really requires a lot of planning to implement them in a way
> that allows a user certain flexibility. To me, neural nets are more of an
> "research tool" in contrast to the currently implemented algos in
> scikit-learn. I really would like to see some way of implementing
> frameworks for neural networks in some useful way in scikit-learn, but I
> can understand that it would really require a different API, a lot of
> planning, and a lot of work. Also, there are many attempts to implement
> neural nets already, like pylearn2, lasagne, and all the other theano
> wrappers
>
>
>
>
> > On May 6, 2015, at 11:15 PM, 赵孽 <snakehunt2...@gmail.com> wrote:
> >
> > I used to seeking neural network algorithms in sklearn, but I just found
> a RBF in it.
> > There are plenty of Neural Network algorithms, why dose we only support
> RBF which is not even a typical neural network ?
> > I thought the neural networks should be the largest family amount
> sklearn algoritms, but it is far smaller than embedings, far smaller than
> SVMs.
> >
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