MLPY - high-performance Python library for predictive modeling

https://mlpy.fbk.eu/

by helwr • 15 years ago

102 9 15 years ago

mlpy.fbk.eu

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

bravura2• 15 years ago

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.

coop11• 15 years ago

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.

ogrisel• 15 years ago

Thanks for the feedback :)

phren0logy• 15 years ago

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.

bravura2• 15 years ago

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...

daemon• 15 years ago

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

egypturnash• 15 years ago

My Little PonY?

kqueue• 15 years ago

On a related note

http://academicearth.org/courses/machine-learning

MLPY - high-performance Python library for predictive modeling

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