
Smile – Statistical Machine Intelligence and Learning Engine - based2
http://haifengl.github.io/smile/
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dsacco
This page has a lot of thinly veiled blackhat SEO. I suppose that doesn't say
anything about the project's technical merits, but it really puts me off.
There are three separate paragraphs that consist of lists of technical terms
and nothing else.

~~~
hatmatrix
> Smile is the most exciting project on Github today!

> Smile covers every aspect of machine learning.

> Smile provides hundreds advanced algorithm with clean interface.

Lots of exclamation points and kind of vague yet sloppy writing... if I didn't
know any better I'd say it's bordering on snake oil.

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mikeskim
This is supposed to be faster than XGBoost? I'm skeptical, but I'd like to
know the specifics of the benchmarks and maybe an outline of the code /
reasons why. It was not benchmarked by the same person who did
[https://github.com/szilard/benchm-ml](https://github.com/szilard/benchm-ml)

~~~
kotach
Vowpal Wabbit is IO limited. Meaning that there's no way it is slower than
anything else on a single machine. On multiple machines it glides faster than
light.

So, the benchmark is probably incorrect for VW.

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based2
[http://xyclade.github.io/MachineLearning/](http://xyclade.github.io/MachineLearning/)

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alex_hirner
Looks like a mammoth project given the amount of contributors. However, what
is more missing in the face of h2o, dl4j and mllib would be a practical
scientific computing / compute graph based library for Scala.

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ljw1001
Looks very interesting to me. Obviously an enormous effort on behalf of the
dev. I'm looking forward to trying it.

