[mlpack] Guidance for the GSOC for Deep learning modules and tree traversers

[Nikhil Yadala]

hi marcus,

      Thanks for the reply. As you have said i have gone through the
information in the above links. I would like to stick for the
implementation of deep learning modules project.

    I have known the basics of deep learning and i have read some chapters
of the book you have mentioned.Should I be knowing all the concepts of deep
learning?, if yes, i guess i should complete reading the book, Nevertheless
I am now good at understanding the things given in the language of deep
learning.Could you tell me what is exactly required, so that i would
concentrate on that topic to a deeper extent.?

thanks,
nikhil yadala.

On Wed, Feb 17, 2016 at 8:01 PM, Marcus Edel <marcus.edel at fu-berlin.de>
wrote:

> Hello Nikhil,
>
> What I really like about the "Essential Deep Learning Modules" project is
> that
> it offers the chance to learn about various fundamental deep learning
> models
> from a practical perspective. As the usual suspects are the most commonly
> used
> algorithms by the community, it is likely that people and developer will
> execute
> your code or use the code that you wrote as basis for their own structures.
>
> Since you already build mlpack, one place to start is to look at the
> tutorials
> and try to compile and run simple mlpack programs.
>
> http://www.mlpack.org/tutorials.html
>
> Then, you could look at the list of issues on Github and maybe you can
> find an
> easy and interesting bug to solve.
>
> https://github.com/mlpack/mlpack/issues
>
> The issues are labeled with difficulty, so you may be able to find some
> which are suited to your level of experience with mlpack.
>
> The literature for the Essential Deep Learning Modules project depends on
> the
> network models you like to implement over the summer. But here are some
> hopefully useful links to get information about the basics:
>
> - "Deep learning reading list" (http://deeplearning.net/reading-list/)
> - "Neural Networks for Machine Learning" by Geoffrey Hinton (
> https://www.coursera.org/course/neuralnets)
> - "Deep learning" by Yoshua Bengio, Ian Goodfellow and Aaron Courville (
> http://www.deeplearningbook.org/)
>
> If you have found network models you're interested in, you
> could start reading about them and we can go from there.
>
> Since Ryan is the mentor of the "Improvement of tree traversers" and the
> expert,
> I go and just make a quote here:
>
> "A good place to start is by working through the mlpack tutorials and
> making sure you can get mlpack to compile and understand how to use it.
> Once you've done that, you should probably read about the ideas behind
> dual-tree algorithms, since your interest is in the 'improvement of tree
> traversers' project.  You might start here:
>
> http://machinelearning.wustl.edu/mlpapers/paper_files/icml2013_curtin13.pdf
>
> There are a lot of references in that document, and you should probably
> read most of them to get a good idea of what is going on (especially the
> cover tree paper by Langford)."
>
> Since you said you are working on a python to c++ translator, you might be
> also
> interested in the automatic bindings project. That project would give a
> way to
> automatically generate MATLAB, Python, and R bindings, which would be much
> easier to maintain than having to maintain each binding individually:
>
> https://github.com/mlpack/mlpack/wiki/SummerOfCodeIdeas#automatic-bindings
>
> Let me know if you have any more questions that I can answer.
>
> Thanks!
> Marcus
>
> On 16 Feb 2016, at 22:40, Nikhil Yadala <nikhil.yadala at gmail.com> wrote:
>
> Hi,
>
>            I am nikhil yadala, pursuing 2nd year of my Btech at dept of
> CSE at Indian Institute of Technology Guwahati( IIT Guwahati). I am very
> much interested in machine learning particularly in computational biology.
> Currently i am doing research in Gene profiling using deep learning methods.
>
>           I am interested in two of the projects that are floated as
> potential ones for the GSOC,2016
>           1)Improvement of Tree traversers
>           2)Essential Deep Learning Modules
> I have enough expertise in C,C++,python,Currently iam developing a PYTHON
> TO C++ TRNSLATOR In c++.
> I would be glad if any one over there would guide me as to get enough
> knowledge to get going with these projects. I have already built mlpack
> over my ubuntu and started to understand the code.
>
> Thanks,
> NIkhil yadala
>
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