[mlpack] Warm up challenges for 'Reinforcement Learning' and 'Essential deep learning modules'

Satyaki Chakraborty satyaki.cs15 at gmail.com
Thu Mar 2 12:47:28 EST 2017

Hello developers,
Hello Marcus,

Good to see mlpack in 2017’s organisation’s list. I am final year bachelor’s student, will be joining Carnegie Mellon University as a graduate student this fall with a specialisation in deep learning. 
I had participated in GSoC 2015 with JdeRobot. 
I also have done an internship from CMU Robotics institute on deep learning, publication : https://www.ri.cmu.edu/pub_files/2016/11/cmu-ri-tr-Maturana.pdf <https://www.ri.cmu.edu/pub_files/2016/11/cmu-ri-tr-Maturana.pdf>

Some of my relevant deep learning projects are:
https://github.com/shady-cs15/tiny-slash <https://github.com/shady-cs15/tiny-slash> (generating guitar music with LSTM nets)
https://github.com/shady-cs15/lrpr <https://github.com/shady-cs15/lrpr> learning deep representations for place recognition (submitted, under review)
https://github.com/shady-cs15/rcnn <https://github.com/shady-cs15/rcnn> RCNNs for scene labelling

I was looking for some projects related to deep learning in GSoC 2017 and the two ideas in mlpack caught my attention.
1. Reinforcement learning
2. Essential deep learning modules

I had used mlpack previously. So I am somewhat familiar with the architecture. 
Could you let me know about any warm up challenges or issues that need to be patch in order to get started or before I start preparing my proposal.

(http://shady-cs15.github.io/ <http://shady-cs15.github.io/>)
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