[mlpack] GSOC'16 Deep Learning Aspirant

[Shashank Gupta]

Hello , 

I am Shashank Gupta, pursuing MS By Research at IIIT-Hyderabad, working under supervision of Dr. Vasudeva Varma (Dean R&D at IIIT-H). My research area is applications of Deep Learning in Text analysis. I have done Machine Learning course (intermediate level, Deep Learning covered) at IIIT Hyderabad and I was Teaching Assistant for Machine Learning Course at BITS Pilani during session 2014-15. 

I have decent knowledge of Deep Learning and I am familiar with basic NN architectures like CNN, RNN and RBM. I am familiar with Python, C++, Java and Matlab. For Deep Learning I have worked with Theano and Keras and I have basic knowledge of tensorflow. I am looking for a Deep Learning project in GSOC this year (as it matches with my research area) and came across MLPACK. 

I have a project proposal. Recently there has been a lot of work in the area of Representation learning for NLP. They are now heavily used in almost all Machine Learning algorithms for texts (and even with Deep learning methods on text). Yet these are not given much thought in popular libraries. I think there should be one module which can learn Representations in unsupervised manner from text. One method which I find easy to understand and works well is Glove. Reference paper for the same is: 

http://www-nlp.stanford.edu/pubs/glove.pdf 

This will be a good starting point to integrate word embedding modules in existing code base and other methods can be added later (prediction based methods etc.). If this proposal is feasible than I would like to contribute to it. 

If the above idea is not feasible due to some reasons than I am also interested in the idea "Essential Deep Learning Modules". I am very much interested in working on such projects as it will give me chance to understand these models closely and get chance to implement them which is always best way to learn. 

I would appreciate if anyone can assess the feasibility of my proposal and also point out how to start with Deep Learning in mlpack. 

Regards, 
Shashank 
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