Hello Sagar,

welcome and thanks for the interest.

> I titled the project as "Generative Neural Networks - RBM and GAN". Any 
> thoughts
> on this?

Sounds good, note that the focus of the project is to take a look at recent
improvements/ideas, meaning that implementing e.g. standard RBM might be an
option but it should be implemented with the extensions in mind.

> Can I also have some pointers on writing a proposal on this topic? I went
> through the guidelines on the wiki, but since this project involves 
> implementing
> two algorithms, I would like to know the best way to go about writing a good
> proposal

There are a lot of good proposals out there that might be helpful, the Student
Manual (https://developers.google.com/open-source/gsoc/resources/manual) also
links to some good examples.

I hope this is helpful, let us know if you have any further questions.

Thanks
Marcus

> On 25 Mar 2017, at 20:16, Sagar B Hathwar <[email protected]> wrote:
> 
> Hello
> 
> I looked at the Essential Deep Learning Modules under GSoC project ideas. 
> Since generative models are a hot field today, I felt that implementing 
> Restricted Boltzmann Machine(plus Deep Belief Networks) and Generative 
> Adversarial Networks (for unsupervised learning)  would be a good combination 
> since both are generative but quite different. I went through the resources 
> provided under the topic and also additional papers on it. Both these 
> algorithms are very interesting.
> 
> I titled the project as "Generative Neural Networks - RBM and GAN". Any 
> thoughts on this?
> 
> Can I also have some pointers on writing a proposal on this topic? I went 
> through the guidelines on the wiki, but since this project involves 
> implementing two algorithms, I would like to know the best way to go about 
> writing a good proposal
> 
> Thanks
> Sagar
> _______________________________________________
> mlpack mailing list
> [email protected]
> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack

_______________________________________________
mlpack mailing list
[email protected]
http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack

Reply via email to