Hi Arnaud,
You are right, I was supposed to finish my MLP PR before the summer, but
my thesis took over my time, which, fortunately, I am completing this
semester :).
Anyhow, I would start with multi-layer perceptron and deep networks
before delving into developing other algorithms.
For the layer configuration, it is absolutely necessary, as there are
many types of layers. But as @Gael said, it could be too complex and
time consuming for the summer. And I have not done it before; so, I
can't anticipate the time it takes. On the other hand, I have already
worked on the algorithms I proposed, meaning I will not face unexpected
obstacles :).
Thanks,
~Issam
On 3/21/2014 1:14 PM, Arnaud Joly wrote:
Hi Issam,
Why not starting by improving multilayer neural network before adding
new algorithms ?
To neural network expert, is it interesting to have layer
configuration à la Torch
https://github.com/torch/nn/blob/master/README.md ?
Best,
Arnaud
On 21 Mar 2014, at 10:18, Issam <issamo...@gmail.com
<mailto:issamo...@gmail.com>> wrote:
Hi Mathieu,
The regularized version is fundamentally different from the
non-regularized version, in that it uses the derivative of the
objective function for which it solves using least-square solution.
The basic ELM version is classic; so, it wouldn't hurt having them
both :). Further, it is not clear that the regularized version always
perform better. And, many extentions of ELM assume the
non-regularized version - like kernel-based ELMs and Sequential ELM.
It shouldn't take two weeks, sorry. After implementing the
non-regularized version it would take two more days at worst.
Since I am removing sparse auto-encoders from the proposal, I will
add two other variants of Extreme Learning Machines,
1) Weighted Extreme Learning Machines for Imbalanced Data
2) Kernel-Based Extreme Learning Machines, with,
2a) Radial Basis Function Kernel
2b) Polynomial kernel
If there is time I will implement an Extreme Learning Machines
version that increments hidden neurons without recalculating the
whole least-square solution [1]. In other words, it can quickly find
the best number of hidden neurons for a particular problem.
Thanks.
[1]Error minimized*extreme learning machine*with growth of hidden
nodes and incremental*learning*
<http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5161346>
On 3/21/2014 5:42 AM, Mathieu Blondel wrote:
Naive questions from someone who knows nothing about ELM. What's the
motivation for implementing both non-regularized and regularized
ELM? If the former tends to overfit, I would keep only the latter.
And why do you need 2 weeks for implementing the regularized
variant? Is the algorithm fundamentally different from the
non-regularized variant?
Mathieu
On Fri, Mar 21, 2014 at 4:44 AM, Issam <issamo...@gmail.com
<mailto:issamo...@gmail.com>> wrote:
Hi all,
I uploaded the proposal for Neural Networks to melange, here is
the public link.
Thank you.
Regards,
~Issam
On 3/20/2014 10:47 AM, Jaidev Deshpande wrote:
On Thu, Mar 20, 2014 at 5:54 AM, Issam <issamo...@gmail.com
<mailto:issamo...@gmail.com>> wrote:
Hi all,
I uploaded the Neural Network proposal to this link,
https://github.com/scikit-learn/scikit-learn/wiki/GSoC-2014:-Extending-Neural-Networks-Module-for-Scikit-learn
Please see if it is detailed enough as a promising proposal.
Thank you.
~Issam
On 3/19/2014 9:00 PM, Jaidev Deshpande wrote:
On Sat, Mar 15, 2014 at 6:59 PM, Issam
<issamo...@gmail.com <mailto:issamo...@gmail.com>> wrote:
Thanks Olivier, I will upload the proposal very soon.
While doing so, I will strengthen my proposal by
implementing a basic
version of each of the proposed algorithms, which I
will cite in my
proposal.
Cheers. :)
Hi Issam,
What's the update on your proposal? I don't mean to rush
you at all, you're probably working hard on the proposal
as we speak, this is just a gentle bump.
All the best.
On 3/14/2014 5:38 PM, Olivier Grisel wrote:
> Issam if I am not mistaken you have not written an
official proposal
> for this GSoC application.
>
> If you are still interested, there is an official
template to follow
> for PSF affiliated sub-projects (such as scikit-learn):
>
>
https://wiki.python.org/moin/SummerOfCode/ApplicationTemplate2014
>
> You can also have a look at Manoj's submission:
>
>
https://github.com/scikit-learn/scikit-learn/wiki/GSoC-2014-Application:-Improved-Linear-Models
>
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Hi Issam,
Looks OK at first glance, but please add it as a gist. Those
are much easier to comment on.
Thanks
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