Hello Abhinav, sounds good, we look forward to your proposals.
Thanks, Marcus > On 24 Mar 2017, at 19:00, Abhinav Moudgil <[email protected]> wrote: > > Hi Marcus, > > I am glad to hear back from you. Taking a detailed look at both the projects, > I have decided to go ahead with the second project i.e. "Essential Deep > Learning Modules". Just to reiterate, I am planning to implement GAN, BRN and > RBM. I will draft a proposal and share it with you on Google Summer of Code > website for feedback. > > Kind regards, > Abhinav Moudgil > > On Fri, Mar 24, 2017 at 10:25 PM, Marcus Edel <[email protected] > <mailto:[email protected]>> wrote: > Hello Abhinav, > > thanks for getting in touch and welcome. > >> I would love to contribute to mlpack during this summer. It would be great if >> you could elaborate your views on the above projects. Looking forward to your >> guidance. > > That are some really neat project's you listed above. I think Ryan tried > something "similar" as training a language model on a jokes corpus. Anyway > here > are my two cents to the projects mentioned above, I think each project is > equally interesting and depending on what you like to do equally difficult and > at the same time rewarding. The intention behind each project is to work on > recent ideas and to provide a fast implementation at the end of the summer. At > the end I can't help you with the decision since you worked on each topic it's > even difficult to give you a recommendation. > > I hope something I said was helpful, > > Thanks, > Marcus > >> On 23 Mar 2017, at 16:25, Abhinav Moudgil <[email protected] >> <mailto:[email protected]>> wrote: >> >> Hi, >> >> I am Abhinav Moudgil, a senior undergraduate research student in Deep >> Learning and Computer Vision, working on PR #942 >> <https://github.com/mlpack/mlpack/pull/942>. I went through mlpack project >> ideas >> <https://github.com/mlpack/mlpack/wiki/SummerOfCodeIdeas#essential-deep-learning-modules> >> and I found the following two projects really interesting (in preference >> order) for Google Summer of Code 2017: >> >> 1. Reinforcement Learning (RL) >> It would be a great learning experience for me to implement RL algorithms, >> which are fast and scalable. Previously, I have studied various RL >> algorithms well like Monte Carlo Policy Gradient (PG) with REINFORCE >> <https://gist.github.com/abhinavmoudgil95/138db4c55c42f91f4c858294acadb771>, >> Deep Q-learning (for discrete and continuous state space), Deep >> Deterministic PG with Actor-Critic networks, Policy Iteration for Maze >> environment, Hill Climbing >> <https://gist.github.com/abhinavmoudgil95/108123c880488965b8c1744cacd60dd6>, >> Random Search >> <https://gist.github.com/abhinavmoudgil95/6fcb2db7314e6c4f6b7a028dfe1f27db> >> etc. I have implemented and tested them in Python using Tensorflow. My >> OpenAI gym profile is accessible here >> <https://gym.openai.com/users/abhinavmoudgil95>. I will open source all my >> RL codes in a separate repository soon. >> >> 2. Essential Deep Learning Modules >> I have studied the relevant literature for this project in the past and I >> like converting mathematical equations from research papers to code. In >> Summer 2016, I worked on feature engineering as a Google Summer of Code >> project <https://abhinavmoudgil95.github.io/2016-08-23/gsoc-conclusion/> >> with CERN SFT where I worked on some advanced feature extraction methods >> like Deep Autoencoders, Feature Clustering, Hessian Locally Linear Embedding >> etc. So, I explored literature on Restricted Boltzmann Machines, Hopfield >> Networks etc. In this project, I would like to implement the following >> models: >> RBM - Studied extensively during my Google Summer of Code, 2016. >> GAN - This semester, I am a Teaching Assistant for the course Statistical >> Methods in AI at my university IIIT-H <https://www.iiit.ac.in/>. As a part >> of this job, I am mentoring projects like Coupled GANs, Conditional GANs. I >> have studied the GAN literature well along with its variations like DCGANs, >> Improved Techniques for training GANs by OpenAI, Class Conditional GANs by >> Yann Lecun etc. >> BRN - I solved <https://abhinavmoudgil95.github.io/2017-03-01/funnybot/> >> OpenAI Request for Research problem #2 >> <https://openai.com/requests-for-research/#funnybot>. For that, I studied >> Recurrent Neural Networks in detail along with variations of it like LSTMs, >> Attention Models, BRNs. Currently, I am working on OpenAI Request for >> Research #3 <https://openai.com/requests-for-research/#im2latex> which >> involves implementing Attention Models and Bidirectional RNNs. >> Open Source Experience: >> I worked <https://github.com/abhinavmoudgil95/gsoc-2016> with CERN SFT on >> feature engineering module as a Google Summer of Code student. I contributed >> <http://wiki.opencog.org/wikihome/index.php/Special:Contributions/Amod95> to >> OpenCog foundation by fixing several bugs and writing an installation script >> <https://github.com/opencog/ocpkg/pull/50> for Mac OS X. I also contributed >> to Shogun, a Machine Learning toolbox where I worked on improving and >> benchmarking <https://github.com/shogun-toolbox/shogun/issues/3048> basic ML >> algorithms like PCA, LDA etc. >> >> I would love to contribute to mlpack during this summer. It would be great >> if you could elaborate your views on the above projects. Looking forward to >> your guidance. >> >> Kind regards, >> >> Abhinav Moudgil >> Github: https://github.com/abhinavmoudgil95 >> <https://github.com/abhinavmoudgil95> >> Website: https://abhinavmoudgil95.github.io/ >> <https://abhinavmoudgil95.github.io/> >> LinkedIn: https://www.linkedin.com/in/abhinavmoudgil/ >> <https://www.linkedin.com/in/abhinavmoudgil/> >> > >
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