I know there's been a lot of discussion [here]( https://github.com/JuliaStats/Roadmap.jl/issues/11) in the past, though not very recently. I would imagine there would be even more willing to participate in pushing things forward at this point (myself included). I'd say chiming in there would most likely get some great response.
-Jacob On Mon, Jan 5, 2015 at 6:28 PM, Tom Fawcett <tom.fawc...@gmail.com> wrote: > True, but yes, not very satisfying. > > It seems like there's a good intersection of Julia people with machine > learning people. I was thinking there might already be an effort underway > to develop a native ML framework for Julia. Since I'm an ML person I'd > like to get involved. But I'm new to Julia so I probably wouldn't be the > best person to lead such an effort. > > Regards, > -Tom > > On Mon, Jan 5, 2015 at 2:42 PM, Stefan Karpinski <ste...@karpinski.org> > wrote: > >> You can always call scikit learn from Julia using PyCall. Not sure how >> satisfying that would be for what you had in mind though. >> >> On Mon, Jan 5, 2015 at 3:22 PM, Tom Fawcett <tom.fawc...@gmail.com> >> wrote: >> >>> Fellow humans, >>> >>> I realize there are various machine learning algorithms implemented in >>> Julia. Is there anything like a machine learning framework, similar to >>> scikit-learn, under development? >>> >>> Of course, Julia already has many of the capabilities of Numpy & Scipy >>> so that's most of the way. I'm imagining a package (or meta-package) to >>> provide a common processing framework (comprising IO, pre-processing, core >>> ML algs, evaluation, visualization, etc.) with a set of APIs. It would >>> provide a standard way to string together components so anyone can set up >>> an ML processing stream or contribute a new module. >>> >>> Is anything in the works? I did a brief search and didn't find >>> anything. >>> >>> Thanks, >>> -Tom >>> >>> >> >
