[mlpack] Regarding guidance for GSOC '18

[Marcus Edel]

Hello Hitesh,

welcome and thanks for getting in touch.

> As far as Reinforcement Learning is concerned, I have played around with the
> cart-pole problem and have a working knowledge of q-learning. To be frank, I am
> not at all familiar with any of the 3 'recent ideas' given in the above link. I
> have tinkered a lot with Neural Networks in Python and built CNN's, RNN's and
> Variational Autoencoders using TensorFlow and worked on the standard problems of
> text and image classification and sentiment analysis. Also, I have taken a look
> at mlpack's existing RE implementations as directed by the ideas page. I believe
> that given adequate time and guidance I can pick up the ideas pretty well and
> quick.

A good first step would be to get familiar with the ideas mentioned on the
project page, you don't have to get every detail but a general idea would help
in the proposal preparing step. If you have any questions, please don#t hesitate
to ask. Also, note this is a C++ library, so you should be familiar with the
common patterns used over the codebase; see mlpack.org/gsoc.html for more
information.

Thanks,
Marcus

> On 11. Mar 2018, at 12:11, Hitesh Bhagchandani <hiteshbhagchandani39 at gmail.com> wrote:
> 
> Hello! I am Hitesh Bhagchandani from BITS , Hyderabad, India, currently pursuing my 3rd year in computer science. I am writing to you regarding the idea : "Reinforcement Learning" that I came across here <https://github.com/mlpack/mlpack/wiki/SummerOfCodeIdeas#reinforcement-learning>.
> 
> I intend to participate in this year's GSOC and would be very grateful if you could help me figure out the next steps I could take in preparing my proposal.
> 
> As far as Reinforcement Learning is concerned, I have played around with the cart-pole problem and have a working knowledge of q-learning. To be frank, I am not at all familiar with any of the 3 'recent ideas' given in the above link. I have tinkered a lot with Neural Networks in Python and built CNN's, RNN's and Variational Autoencoders using TensorFlow and worked on the standard problems of text and image classification and sentiment analysis. Also, I have taken a look at mlpack's existing <https://github.com/mlpack/mlpack/tree/master/src/mlpack/methods/reinforcement_learning> RE implementations as directed by the ideas page. I believe that given adequate time and guidance I can pick up the ideas pretty well and quick.
> 
> Regards,
> Hitesh Bhagchandani.

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