[mlpack] Essential Deep Learning Modules in GSoC '19

[Marcus Edel]

Hello Rajiv,

thanks again for the contributions. Implementing Graph Neural Networks is quite
a challenge especially timewise but if you are up for that; we should make sure
the timeline is reasonable including the milestones. Everything needs to be
tests and stable at the end of the summer which often takes a lot of time, but
as I said if you are up for the challenge this could be an interesting project
for sure.

Let us know what you think.

Thanks,
Marcus

> On 28. Feb 2019, at 19:14, Rajiv Vaidyanathan <rajiv.vaidyanathan4 at gmail.com> wrote:
> 
> Dear Marcus, Mikhail and Shikhar,
> 
> I am N Rajiv Vaidyanathan and I'm interested in the topic "Essential Deep Learning Modules" in GSoC 19.
> 
> Over the past month, I'm trying to get an understanding of the MLPack codebase by making contributions. As of now, I have implemented SPSA optimiser, Dice Loss function and currently working on Dense blocks.
> 
> I recently read a paper on Graph Neural Networks and found it to be fascinating as it works very well on non-Euclidian spaces such as social networks and 3D images. I am interested in working on implementing this network along with tests and documentation. I'm also interested in improving the overall documentation of ann.
> 
> Please let me know what you think :)
> 
> Thanks and regards,
> Rajiv
> ᐧ
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