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MLPY - high-performance Python library for predictive modeling (fbk.eu)
102 points by helwr on Apr 23, 2011 | hide | past | web | favorite | 9 comments

I'm not aware of anyone who uses mlpy, but I would be curious to hear about experiences with it.

In Python, for general Machine Learning I really like scikits.learn (http://scikit-learn.sourceforge.net/). The library is Pythonic and includes many common ML tools I need.

I have heard good things about Shogun (http://www.shogun-toolbox.org/), which is written in C++ and has bindings for many other languages, including Python. Shogun appears to be more focused on kernel algorithms (e.g. SVMs).

There's a GSOC project to bind Vowpal Wabbit in Shogun. That would be sweet. This was mentioned by John Langford on the VW mailing list.

Theano (http://deeplearning.net/software/theano/) is great if you are devising your own ML models, particularly ones that are based upon gradient descent. It's Python and it automatically compiles to C/C++ and then machine code for your CPU or GPU.

I agree about scikits.learn. Olivier Grisel did a great job introducing it at Pycon and I find that the documentation makes it more inviting for those new to machine learning.

Thanks for the feedback :)

Thanks for the heads up about Theano, I hadn't seen it before. I keep thinking I'll see more machine learning work that compiles to GPU, but I haven't seen much yet.

This looks really fantastic.

Theano is great. It is primarily developed at the Universite de Montreal in Yoshua Bengio's lab, where I did my postdoc.

We discussed Python ML packages on MetaOptimize: http://metaoptimize.com/qa/questions/3053/python-machine-lea...

Here is a little more detailed summary about Theano, which I wrote up on that thread:

Theano is a CPU and GPU compiler for mathematical expressions in Python. It combines the convenience of NumPy with the speed of optimized native machine language. For gradient-based machine learning algorithms (like training an MLP or convolutional net), Theano is from 1.6x to 7.5x faster than competitive alternatives (including those in C/C++, NumPy, SciPy, and Matlab) when compiled for the CPU and between 6.5x and 44x faster when compiled for the GPU. The SciPy 2010 paper is a good introduction: http://www.iro.umontreal.ca/~lisa/pointeurs/theano_scipy2010...

Previous, related discussion: http://news.ycombinator.com/item?id=490851

A good chance to teach both Machine Learning and Python, should be interesting to see if any lecturers actually try it..

My Little PonY?

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