all code

LICENSE_Original_Deep_Learning_Tutorials.txt

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+LICENSE
+
+Copyright (c) 2008–2013, Theano Development Team All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+        Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+        Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+        Neither the name of Theano nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ‘’AS IS’’ AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.txt

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+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
+
+===========================================================
+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
+===========================================================

README.txt~

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+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
+
+===========================================================
+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
+===========================================================