[mlpack] Towards a robust deep learning module for FastML | Guidance for GSOC'16

[Shikhar Murty]

Hi Marcus,

I'm a junior year EE undergrad (with elective concentration in CS) at IIT,
Delhi. I'm an undergraduate researcher in Deep learning and NLP. I have
implemented a bunch of ANNs and recently have started using FastML. I have
familiarised myself with the code base of FastML, and understand the ANN
implementation and the RNN implementation along with the optimisers
module.  I'm quite keen on participating in GSOC'16 under FastML, and
request you to mentor me for the same.

As an undergraduate researcher, I've read and understood multiple papers in
Deep learning, for instance Collobert et al (NLP from scratch), Mikolov et
al. (word2vec), Dropouts, Adam, Bengio's paper on neural network language
models and so on.

I have great interests in contributing to the FastML code base,
particularly in extending or improving deep learning modules within FastML.
In particular, I would like to implement GRUs and augment the current
implementation of LSTMs to include peephole connections which have become
very popular. I would also like to implement the CBOW and the word2vec
models for FastML as they have also become quite popular in the NLP
community.

Could you please give me some pointers on how I should structure my
application for GSOC to boost my chances?

Thank you for your time and I look forward to a positive response

Regards,

Shikhar Murty
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.cc.gatech.edu/pipermail/mlpack/attachments/20160313/3bf99df8/attachment-0002.html>