MDP – Modular toolkit for Data Processing

author: Tiziano Zito, Bernstein Center for Computational Neuroscience Berlin, Humboldt University of Berlin
published: Dec. 20, 2008,   recorded: December 2008,   views: 504
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user’s per- spective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network archi- tectures. From the scientific developer’s perspective, MDP is a modular framework, which can easily be expanded.

The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily in- creasing and includes, to name but the most common, Principal Component Analysis (PCA and NIPALS), several Independent Component Analysis algorithms (CuBICA, FastICA, TDSEP, and JADE), Slow Feature Analysis, Gaussian Classifiers, Restricted Boltzmann Machine, and Locally Linear Embedding.

See Also:

Download slides icon Download slides: mloss08_zito_mdp_01.pdf (718.3 KB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: