deep_learning
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yifeng-li
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.
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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
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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
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A utility module for classification is included. This module is named classification.py. See the beginning of this file for usage.
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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
=========================================================== 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: [email protected], [email protected] Home Page: http://www.cmmt.ubc.ca/directory/faculty/yifeng-li
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