There is also keras https://github.com/fchollet/keras and lasagne
https://github.com/Lasagne/Lasagne
I think sklearn-theano wraps some of lasagne?
On 05/07/2015 10:34 AM, Michael Eickenberg wrote:
\begin{plug}
And in a similar vein there is also http://sklearn-theano.github.io
for those who want to leverage existing, pre-trained networks as
feature extractors for their scikit-learn pipeline, which can, e.g. be
followed by a simple logistic regression to fine-tune to new types of
objects.
\end{plug}
Michael
On Thu, May 7, 2015 at 3:45 PM, Boyuan Deng <bryanhsud...@gmail.com
<mailto:bryanhsud...@gmail.com>> wrote:
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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