[mlpack] Getting Introduced to the Community

[PRAKHAR AGARWAL]

Hi Marcus,

Thanks for the resources!
I really enjoyed reading about Bidirectional Neural networks and Dropout.
I would like to start by trying to implement Dropout for mlpack and get
back with issues as and when required.
So I would request you to add some simple tasks which might be helpful for
me to get familiar with the internal neural network architecture for mlpack
?

Thanks,

Prakhar

On Wed, Feb 25, 2015 at 4:40 AM, Marcus Edel <marcus.edel at fu-berlin.de>
wrote:

> Hi Prakhar,
>
> Unfortunately there aren't any deep learning related issues. I will spend
> some
> time over the weekend to add some simple tasks which might be helpful to
> get
> familiar with the internal neural network architecture.
>
> Since you're familiar with recurrent networks maybe you are interested in
> adding
> bidirectional recurrent networks:
>
> "Bidirectional Recurrent Neural Networks"
> http://www.di.ufpe.br/~fnj/RNA/bibliografia/BRNN.pdf
> This is the first paper, which introduces the idea of building a
> bidirectional
> recurrent network. Maybe you might find it interesting.
>
> Another interesting and simple task could be to implement dropout:
>
> "Dropout: A Simple Way to Prevent Neural Networks from Overfitting"
> http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf
> An interesting technique to tackle overfitting. The key idea is to
> randomly drop
> units during training.
>
> If you'd like we can discuss more on this.
>
> Thanks,
> Marcus
>
> On 24 Feb 2015, at 17:47, Ryan Curtin <ryan at ratml.org> wrote:
>
> On Tue, Feb 24, 2015 at 03:33:41PM +0530, PRAKHAR AGARWAL wrote:
>
> Hi,
>
> My name is Prakhar Agarwal and I would like to introduce myself to the
> developers of this community. I'm (technically) a junior at Birla Institute
> of Technology and Science Pilani. I am well versed with Python, C/C++,
> Machine Learning, PHP and bash. I have been using the mlpack and now I'm
> comfortable with it.
>
> I was very much fascinated with the concept of Deep Learning  and wanted to
> explore it more and what could be a better way to start off than writing an
> algorithm and seeing it in action.
>
> I was having a look at the GSoC 2015 ideas
> <https://github.com/mlpack/mlpack/wiki/SummerOfCodeIdeas> page and the
> particular project of Essential Deep Learning Modules
> <
> https://github.com/mlpack/mlpack/wiki/SummerOfCodeIdeas#essential-deep-learning-modules
> >.
> I
> have some basic information about Belief networks and Recurrent networks
> and have worked on artificial neural networks. I have also tried building
> mlpack from source and writing basic mlpack programs.
>
> Could you please guide me through where should I look forward that would be
> helpful for the project?
>
>
> Hi Prakhar,
>
> Since you are interested in neural networks, I would spend some time
> taking a look at the code that Marcus and Shangtong have written, both
> in src/mlpack/methods/ann/ and in pull request #405.  Other than that,
> you might consider taking a look through the list of open issues on
> Github and seeing if any of them are interesting to you.  They are
> labeled with difficulty, so this should help in finding ones that are
> easier for people who aren't intricately familiar with the internals of
> mlpack.
>
> Hope that helps -- if not, please feel free to ask more questions. :)
>
> Thanks,
>
> Ryan
>
> --
> Ryan Curtin    | "Open the pig!"
> ryan at ratml.org |   - Frank Moses
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>
>
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