| Line 10: | Line 10: | ||
== Interesting software waiting for being packaged == | == Interesting software waiting for being packaged == | ||
* [https://pypi.python.org/pypi/bob bob - free signal-processing and machine learning toolbox] | * [https://pypi.python.org/pypi/bob bob - free signal-processing and machine learning toolbox] | ||
* [https://pypi.python.org/pypi/copper copper - Fast, easy and intuitive machine learning prototyping] | * [https://pypi.python.org/pypi/copper copper - Fast, easy and intuitive machine learning prototyping] | ||
| Line 24: | Line 22: | ||
* [https://pypi.python.org/pypi/Monte Monte - machine learning in pure Python] | * [https://pypi.python.org/pypi/Monte Monte - machine learning in pure Python] | ||
* [https://pypi.python.org/pypi/nolearn nolearn - Miscellaneous utilities for machine learning] | * [https://pypi.python.org/pypi/nolearn nolearn - Miscellaneous utilities for machine learning] | ||
* [https://pypi.python.org/pypi/pcSVM pcSVM] | * [https://pypi.python.org/pypi/pcSVM pcSVM] | ||
* [https://pypi.python.org/pypi/Peach Peach - Python library for computational intelligence and machine learning] | * [https://pypi.python.org/pypi/Peach Peach - Python library for computational intelligence and machine learning] | ||
| Line 32: | Line 28: | ||
* [https://pypi.python.org/pypi/ramp Rapid Machine Learning Prototyping] | * [https://pypi.python.org/pypi/ramp Rapid Machine Learning Prototyping] | ||
* [https://pypi.python.org/pypi/Reinforcement-Learning-Toolkit Reinforcement-Learning-Toolkit] | * [https://pypi.python.org/pypi/Reinforcement-Learning-Toolkit Reinforcement-Learning-Toolkit] | ||
* [http://sourceforge.net/projects/weka/ Weka---Machine Learning Software in Java] | * [http://sourceforge.net/projects/weka/ Weka---Machine Learning Software in Java] | ||
== Work in progress == | == Work in progress == | ||
* [http://mahout.apache.org/ Apache Mahout] | |||
* [https://pypi.python.org/pypi/astroML astroML] | |||
* [http://orange.biolab.si/ Orange] | |||
* [http://www.clips.ua.ac.be/pages/pattern Pattern - Web mining module for Python] | |||
* [http://scikit-learn.org/ scikit-learn -Machine Learning in Python] | |||
* [http://shogun-toolbox.org/ SHOGUN] | |||
== New packages == | == New packages == | ||
Revision as of 12:44, 24 September 2013
Machine Learning SIG
The Machine Learning SIG, aims to make Fedora the best platform for all things related to Machine Learning.
Members
Björn Esser (besser82) <besser82@fedoraproject.org>
Machine Learning Packages
Interesting software waiting for being packaged
- bob - free signal-processing and machine learning toolbox
- copper - Fast, easy and intuitive machine learning prototyping
- ease - Machine learning based automated text classification library
- hyperspy - Hyperspectral data analysis toolbox
- infer - machine learning toolkit for classification and assisted experimentation
- Java-ML
- milk - Machine Learning Toolkit
- MLizard - Machine Learning workflow automatization
- mlpy - Machine Learning Python
- Maja Machine Learning Framework
- Monte - machine learning in pure Python
- nolearn - Miscellaneous utilities for machine learning
- pcSVM
- Peach - Python library for computational intelligence and machine learning
- PyBrain
- PyML
- Rapid Machine Learning Prototyping
- Reinforcement-Learning-Toolkit
- Weka---Machine Learning Software in Java
Work in progress
- Apache Mahout
- astroML
- Orange
- Pattern - Web mining module for Python
- scikit-learn -Machine Learning in Python
- SHOGUN
New packages
When submitting a new ml-related package for review, please add "Blocks: ML-SIG" to your review-request. After the review has been granted don't forget to remove it, when filing the SCM-request, please.
When you are filing your SCM-admin-request, you should make sure to request InitialCC for "ml-sig".
Example:
New Package SCM Request ======================= Package Name: pkgname Short Description: summary of package Owners: foo bar Branches: f18 f19 f20 el5 el6 InitialCC: ml-sig
Packages waiting for your review
You can find them on the ML-SIG review-tracker.
We would be glad, if you would take one or a few. :)
Existing packages
You can find the existing ml-related packages on the PkgDB.
Categories
more to come soon.
What are we going to do?
more to come soon.
Participation
There is no formal process for participating; joining the mailing list, hanging out on IRC, or participating in meetings are all fantastic ways to get involved.
A little self-introduction on the mailing list would be nice, too. And, if you want to, add yourself to our members-section above.
Mailing list
IRC
We will likely hang out on irc.freenode.net at #fedora-ml. German members may want to come into #fedora-ml-de, too.
Haven't used IRC for communication before? More information on how to use IRC is available here.
Meetings
We shall have them, and see how it goes.
more to come soon.