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]> 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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