[mlpack] Reinforcement Learning

Ryan Curtin ryan at ratml.org
Sun Mar 5 21:28:11 EST 2017


On Sun, Mar 05, 2017 at 12:23:34PM +0530, S.NARAYAN ee15b108 wrote:
> I pulled an all nighter

I know this point is a little off topic here, but in my opinion this is
not a great way to ingest the material!  Get some sleep and take it
slow; the projects for mlpack tend to be quite complex and the necessary
material needs to be understood in-depth, which (at least for me)
couldn't be done in a single sleepless night.

> As its a new one , no relevant issues exist as of now so I would like to
> start by adding a Cross-Validation module to Linear Regression (will start
> working on it now) , and once i get accustomed to ML-Packs coding style and
> API designs i can add further modules for other training algorithms and
> also optimize my approaches (through better pre-computation etc ).

This isn't really what the project is about, unfortunately.  Please
carefully read the description.  We want to create a generic
cross-validation module that can work with any mlpack classifier.  So
the place to start is not to graft support onto the mlpack linear
regression implementation but instead carefully consider the set of
classifiers that mlpack implements and determine a way to create a
generic cross-validation module that can support them all.

The cross-validation module will probably need to use some amount of
template metaprogramming so you probably want to spend some time
familiarizing yourself with that also.

I believe that I have written an email to the mailing list about this
project (I haven't gone through the effort to find it right now); I'd
suggest searching the mailing archives to find it.  This could help
clarify what the project is all about.

Thanks,

Ryan

-- 
Ryan Curtin    | "Lady, I'm gonna have to ask you to leave the store."
ryan at ratml.org |   - Ash in Housewares


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