Wow, better than scikit.learn? This is exciting. We should probably discuss in the roadmap issue about what infrastructure we need to support large-scale distributed machine learning problems.
-viral On Monday, July 21, 2014 4:08:14 AM UTC+5:30, Dahua Lin wrote: > > Please see https://github.com/JuliaStats/MLBase.jl/blob/master/NEWS.md > for recent updates. > > Also the documentation is moved from Readme to a Sphinx doc > <http://mlbasejl.readthedocs.org/en/latest/> > > Now we already have quite a few packages for various machine learning > tasks: > > MLBase.jl <https://github.com/JuliaStats/MLBase.jl>: data preprocessing, > performance evaluation, cross validation, model tuning, etc > Distance.jl <https://github.com/JuliaStats/Distance.jl>: metric/distance > computation (including batch & pairwise computation) > MultivariateStats.jl <https://github.com/JuliaStats/MultivariateStats.jl>: > multivariate analysis, ridge regression, dimensionality reduction > Clustering.jl <https://github.com/JuliaStats/Clustering.jl>: K-means, > K-medoids, Affinity propagation > NMF.jl <https://github.com/JuliaStats/NMF.jl>: Nonnegative matrix > factorization > > In addition, we have a bunch of other packages for Regression, GLM, SVM, > etc. We are now beginning to unite the efforts in this domain (see the > discussion <https://github.com/JuliaStats/Roadmap.jl/issues/14> here). > > We have been making steady progress, and I believe that we will have a > great machine learning ecosystem, one that is comparable or even superior > to scikit.learn in not too long future. > > Cheers, > Dahua > >
