FYI, in April a project called *"***scikit-neuralnetwork" came into
existence. It's a library wrapping Pylearn2 and providing a scikit-learn
compatible interface. https://github.com/aigamedev/scikit-neuralnetwork
As others have stated, there are already some nice Python neural network
libraries and we don't really need to reinvent a wheel. Pylean2 is
mostly research-oriented and provides building blocks. I think sknn is
able to fill that gap and enable sklearn-like experience with neural
networks.
Personally I hope in the future our community can officially help with
sknn and feature it as an "add-on" to sklearn (because Pylearn2 may
never go into stable release). In that way sklearn can definitely be the
dominating Python machine learning library.
On 05/07/2015 07:10 AM, Joel Nothman wrote:
What Sebastian and Ronnie said. Plus: there are multiple off-the-shelf
neural net pull requests in the process of review, notably those by
Issam Laradji for GSoC 2014. Extreme Learning Machines and Multilayer
Perceptrons should be merged Real Soon Now.
On 7 May 2015 at 14:58, Ronnie Ghose <ronnie.gh...@gmail.com
<mailto:ronnie.gh...@gmail.com>> wrote:
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 <mailto: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
<mailto: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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