Hello Sinjan, please open a PR, that way we can discuss the code very easily and using another folder in mlpack/src/mlpack/methods/ sounds good.
I hope this is helpful, let us know if you have any more questions. Thanks, Marcus > On 28 Feb 2017, at 17:48, Sinjan Chakraborty <[email protected]> wrote: > > Hello Marcus, > > I have forked mlpack to my github account. Now, if I want to implement the > policy gradient method, I understand that I am supposed to add this > implementation to a folder under mlpack and make a pull request. Am I > supposed to implement the method under mlpack/src/mlpack/methods/? > > Or, should I create a separate repository in my github profile, implement the > policy gradient and provide you with the link? > > Thanks, > Sinjan > > On Tue, Feb 28, 2017 at 8:04 PM, Marcus Edel <[email protected] > <mailto:[email protected]>> wrote: > Hello Sinjan, > >> Over the past year I have been involved with two separate projects in Machine >> Learning. I have also completed the online Machine Learning course by Andrew >> Ng >> (with certificate) and Neural Networks for Machine Learning by Geoffrey >> Hinton >> and the deep learning course on Udacity by Google. >> >> I haven't got a chance to put my knowledge in deep learning to any practical >> use >> since the projects I worked were based on clustering algorithms and >> artificial >> neural networks. That's why I am particularly interested about working on >> this >> project. > > Great that you like the project, I think GSoC is a great opportunity to work > on > a project that you really like. > >> I will start learning Reinforcement learning from the Berkeley Deep >> Reinforcement Learning course. <http://rll.berkeley.edu/deeprlcourse/ >> <http://rll.berkeley.edu/deeprlcourse/>> > > Sounds good, you can also checkout the references given in the project > description; each paper also has a bunch of different references that are > worth > to checkout. > >> I would be very grateful if you can guide me if there is anything I am >> required >> to study to make myself ready for this project. > > To be successful at this project, you should have a good knowledge of > reinforcement learning; i.e., you should be familiar with the way agents are > typically built and trained, and certainly, you should be familiar with the > individual components that you plan to implement. > >> I would also like to know if I should start working on the issues. I have >> installed mlpack properly on my system. I have gone through the command-line >> programs and the C++ implementations of the methods. And now I want to start >> contributing to mlpack. > > So there are some easy issues on GitHub that you might find interesting, we > will > see if we can add more in the next days. Besides that, since you like to work > on > the reinforcement learning project, maybe you like to implement an simple > agent, > that is capable of solving some simple tasks; Policy Gradients is a simple > method that is really powerful and also quite intuitive. Don't feel obligated, > you don't have to solve issues or implement anything to be considered for the > project, but it's an easy way to dive into the codebase. > > I hope this is helpful, let us know if you have any more questions. > > Thanks, > Marcus > > >> On 28 Feb 2017, at 12:37, Sinjan Chakraborty <[email protected] >> <mailto:[email protected]>> wrote: >> >> Hi, >> >> My name is Sinjan Chakraborty. I am a junior undergraduate student in >> Computer Science and Engineering from India. I have also communicated with a >> few mentors yesterday on the #mlpack IRC Node through my nickname Sinjan_. I >> would like to work with mlpack on the Reinforcement Learning Project. >> >> Over the past year I have been involved with two separate projects in >> Machine Learning. I have also completed the online Machine Learning course >> by Andrew Ng (with certificate) and Neural Networks for Machine Learning by >> Geoffrey Hinton and the deep learning course on Udacity by Google. >> >> I haven't got a chance to put my knowledge in deep learning to any practical >> use since the projects I worked were based on clustering algorithms and >> artificial neural networks. That's why I am particularly interested about >> working on this project. >> >> I will start learning Reinforcement learning from the Berkeley Deep >> Reinforcement Learning course. <http://rll.berkeley.edu/deeprlcourse/ >> <http://rll.berkeley.edu/deeprlcourse/>> >> >> I would be very grateful if you can guide me if there is anything I am >> required to study to make myself ready for this project. >> >> I would also like to know if I should start working on the issues. I have >> installed mlpack properly on my system. I have gone through the command-line >> programs and the C++ implementations of the methods. And now I want to start >> contributing to mlpack. >> >> >> Thanking you, >> Yours sincerely, >> >> Sinjan Chakraborty. >> _______________________________________________ >> mlpack mailing list >> [email protected] <mailto:[email protected]> >> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack >> <http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack> >
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