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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