daweicheng/deep_learning

Deep Learning Package in Python Based on The Deep Learning Tutorials and Theano

This deep learning package is an extension of the Deep Learning Tutorials (www.deeplearning.net/tutorial/).

This package is composed of four parts as follows.

1. Modified methods from Deep Learning Tutorials:
Multi-class logistic/softmax regression: logistic_sgd.py
Multilayer perceptrons (MLP): mlp.py
Stacked denoising autoencoder (SdA): SdA.py
Stacked contractive autoencoder (ScA): ScA.py
Restricted Boltzman machine (RBM): rbm.py
Deep belief network (DBN, stacked restricted Boltzman machine): DBN.py
Convolutional neural network (CNN): convolutional_mlp.py

2. Our deep-feature-selection models:
Deep feature selection based on MLP: deep_feat_select_mlp.py
Deep feature selection based on ScA: deep_feat_select_ScA.py
Deep feature selection based on DBN: deep_feat_select_DBN.py

3. A utility module for classification is included. 
This module is named classification.py.
See the beginning of this file for usage.

4. Examples:
For every methods in 1 and 2, an example is provided to demonstrate how to use it.
The file names of these examples are main_[module_name].py

Citation:
On the way...

License:
See LICENSE_Original_Deep_Learning_Tutorials.txt
Note, we also reserve the copyright on the part we contributed in this new package.

Other Useful Information:
[1] Deep Learning Tutorials (www.deeplearning.net/tutorial/).
[2] http://deeplearning.cs.toronto.edu/
[3] UFLDL Tutorial: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial

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Contact:
Yifeng Li, Ph.D
Post-Doctoral Research Fellow
Wasserman Lab
Centre for Molecular Medicine and Therapeutics
Department of Medical Genetics
University of British Columbia
Child and Family Research Institute
Vancouver, BC, Canada
Email: yifeng@cmmt.ubc.ca, yifeng.li.cn@gmail.com
Home Page: http://www.cmmt.ubc.ca/directory/faculty/yifeng-li

NMF Toolbox: https://sites.google.com/site/nmftool
SR Toolbox:    https://sites.google.com/site/sparsereptool
RLMK Toolbox: https://sites.google.com/site/rlmktool
PGM Toolbox:   https://sites.google.com/site/pgmtool
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