Hello Rohan,

welcome and thanks for getting in touch.

The video is a really nice introduction, have to keep that in mind. An important
step for every project is to get familiar with the codebase (e.g. going through
the codebase and run tests), for the RBFN project it's a good idea to take a
closer look at the network code:
https://github.com/mlpack/mlpack/tree/master/src/mlpack/methods/ann and
corresponding tests:
https://github.com/mlpack/mlpack/tree/master/src/mlpack/tests. Let me know if I
should clarify anything and please don't hesitate to ask questions.

Thanks,
Marcus

> On 18. Feb 2018, at 17:22, Rohan Rajadhyax <rohanrajadhya...@gmail.com> wrote:
> 
> My name is Rohan Rajadhyax and I am a fourth year engineering  undergraduate 
> student from Mumbai,India.I am interested in applying for GSoC and 
> contributing for MLpack library. 
> I have completed some courses on Python,alogrithms and neutral networks from 
> MIT ocw,NPTEL and I have a research experience in the field of machine 
> learning and image processing.I am currently working of analysis of 
> hyperspectral images using k means clustering.
> I'm new to open source field and interested in exploring it. 
> I'm interested in working on RBFN for GSOC 2018.May I get some leads on this 
> topic.
> After setting up the environment for the required and following the 
> instructions on the contributing to mlpack page.I started implementing simple 
> command line executable to understand the process.
> Currently I'm reference to the following sites for further information.
> Introduction to Radial Basis Function Networks
> https://www.cc.gatech.edu <https://www.cc.gatech.edu/>›rbf-intro
> 
> https://youtu.be/KiVJkqac82Q <https://youtu.be/KiVJkqac82Q>
> I'm interested in working on this idea for GSOC 2018.Kindly consider me for 
> the project.
> Regards,
> Rohan Rajadhyax
> 
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