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
Thanks, Jacob, this is what I was looking for.
-Tom
On Mon, Jan 5, 2015 at 5:48 PM, Jacob Quinn quinn.jac...@gmail.com wrote:
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
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
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
I do think that even just getting the API right will take a while, and writing
Julian wrappers around scikits will be useful.
I do think that even just getting the API right will take a while, and writing
Julian wrappers around scikits will be useful.
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.
