Pylearn2: A machine learning research library
Project Website: http://deeplearning.net/software/pylearn2/
Github Link: https://github.com/lisa-lab/pylearn2
Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. This means you can write Pylearn2 plugins (new models, algorithms, etc) using mathematical expressions, and Theano will optimize and stabilize those expressions for you, and compile them to a backend of your choice (CPU or GPU).
Researchers add features as they need them. We avoid getting bogged down by too much top-down planning in advance.
A machine learning toolbox for easy scientific experimentation.
All models/algorithms published by the LISA lab should have reference implementations in Pylearn2.
Pylearn2 may wrap other libraries such as scikit-learn when this is practical
Pylearn2 differs from scikit-learn in that Pylearn2 aims to provide great flexibility and make it possible for a researcher to do almost anything, while scikit-learn aims to work as a “black box” that can produce good results even if the user does not understand the implementation
Dataset interface for vector, images, video, …
Small framework for all what is needed for one normal MLP/RBM/SDA/Convolution experiments.
Easy reuse of sub-component of Pylearn2.
Using one sub-component of the library does not force you to use / learn to use all of the other sub-components if you choose not to.
Support cross-platform serialization of learned models.
Remain approachable enough to be used in the classroom (IFT6266 at the University of Montreal).