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Pylearn specifications

Information


Unique identifier OMICS_28757
Name Pylearn
Alternative name pylearn2
Software type Framework/Library
Interface Command line interface, Application programming interface
Restrictions to use Academic or non-commercial use
Operating system Unix/Linux
Programming languages Python
Parallelization CUDA
License BSD 3-clause “New” or “Revised” License
Computer skills Advanced
Version 0.6rc3
Stability Stable
Maintained No

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Documentation


Maintainers


This tool is not maintained anymore.

Additional information


http://deeplearning.net/software/pylearn2/library/index.html#libdoc http://deeplearning.net/software/pylearn2/faq.html#faq

Publication for Pylearn

Pylearn citations

 (7)
library_books

Ten quick tips for machine learning in computational biology

2017
BioData Min
PMCID: 5721660
PMID: 29234465
DOI: 10.1186/s13040-017-0155-3

[…] ch as the extremely popular Bioconductor package []). On the other hand, Python is a high-level interpreted programming language, which provides multiple fast machine learning libraries (for example, Pylearn2 [], Scikit-learn []), mathematical libraries (such as Theano []), and data mining toolboxes (such as Orange []). Torch, instead, is a programming language based upon lua [], a platform, and a […]

library_books

Toolkits and Libraries for Deep Learning

2017
J Digit Imaging
PMCID: 5537091
PMID: 28315069
DOI: 10.1007/s10278-017-9965-6

[…] Pylearn2 is a machine learning research library developed by Laboratoire d’Informatique des Systèmes Adaptatifs (LISA) at University of Montreal []. Pylearn2 offers a collection of classical machine l […]

library_books

Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization

2017
PeerJ
PMCID: 5629961
PMID: 29018612
DOI: 10.7717/peerj.3874

[…] does not overlap with training and validation set. On this test set, the best Object-Net achieved an error rate of 4.71%, whereas the Separator-Net achieved 5.58%. The CNN models were implemented in Pylearn2 (), a machine learning library built on top of Theano (; ). […]

library_books

Stochastic Synapses Enable Efficient Brain Inspired Learning Machines

2016
Front Neurosci
PMCID: 4925698
PMID: 27445650
DOI: 10.3389/fnins.2016.00241

[…] fire neurons (Cao et al., ; Diehl et al., ). Such mapping techniques have the advantage that they can leverage the capabilities of existing machine learning frameworks such as Caffe (Jia et al., ) or pylearn2 (Goodfellow et al., ) for brain-inspired computers. Although mapping techniques do not offer a solution for on-line, real-time learning, they resulted in the best performing spike-based imple […]

library_books

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

2016
Sci Rep
PMCID: 4876324
PMID: 27212078
DOI: 10.1038/srep26286

[…] To train the convolutional neural network we made use of the open-source ‘deep learning’ libraries Theano 0.7 and pylearn2 0.1.As it is impossible to feed entire whole-slide images to the network at once, we randomly extracted small patches from the whole-slide image for training. Whole-slide results can then be […]

library_books

An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

2015
Int J Mol Sci
PMCID: 4519904
PMID: 26198229
DOI: 10.3390/ijms160715384

[…] he input layer consists of 1911 features, and the second and third layers each consist of 240 maxout nodes []. The final output is a two-class multinomial node. The deep network was trained using the PyLearn2 library [] with stochastic gradient descent on batches of 1000 training examples. In addition to the maxout nodes, a dropout procedure [] was used, which randomly dropped out the output of so […]

Citations

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Pylearn institution(s)
Departement d’Informatique et de Recherche Operationelle, Universite de Montreal, Montreal, QC, Canada; Center for Theoretical Neuroscience, University of Waterloo, Waterloo, Belgium

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