Hello everyone, Myself Abhinav Kumar, 4th year computer science student from NIT Srinagar, India. I am a machine learning and parallel computing enthusiast and worked on unsupervised learning during my previous internship. I was learning about ml programing then I came through mlpack. Its a nice and promising software. I have experience of using armadillo,cmake and lapack for my ml and c++ programs. I have made contribution to gnome software. I like to contribute to mlpack and be part of GSOC'17 through mlpack organisation. I have compiled mlpack from source on my computer and used some of its algorithm. I am also familiar with git system and have a good experience with c++. I have read about template SFINAE and reading about policy based design (as provided in mlpack documentation).
I was going through mlpack GSOC list 2017 and I like to work on - * Parallel stochastic optimization methods * I am very much interested to work on this project.I have found these paper related to this project - for SCD (https://arxiv.org/pdf/1311.1873.pdf) for SGD (http://martin.zinkevich.org/publications/nips2010.pdf) I have also found that this project was listed in GSOC 2015 and archive related to this is - Archive March 2015 <http://knife.lugatgt.org/pipermail/mlpack/2015-March/001658.html> For Martin Zinkevich paper, it was commented as " I think it makes more sense in a distributed setting (where communication is much more expensive than in a shared memory setting). " So, I have to look for other paper on it. For this project , I need to learn convex optimization and this course by Stanford University <http://online.stanford.edu/course/convex-optimization-winter-2014> can be really helpful. If there is any other resources to look for please direct me to that. I have some experience of creating multi-thread program on c# and i think by using that knowledge and studying tutorials based on c++ threads I can do a great work on this project. It would be a great opportunity to be part of this organisation and contribute to mlpack.
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