autoencoder

Sparse Autoencoder for Automatic Learning of Representative Features from Unlabeled Data

Implementation of the sparse autoencoder in R environment, following the notes of Andrew Ng (http://www.stanford.edu/class/archive/cs/cs294a/cs294a.1104/sparseAutoencoder.pdf). The features learned by the hidden layer of the autoencoder (through unsupervised learning of unlabeled data) can be used in constructing deep belief neural networks.

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Description file content

Package
autoencoder
Type
Package
Title
Sparse Autoencoder for Automatic Learning of Representative Features from Unlabeled Data
Version
1.1
Date
2015-06-30
Author
Eugene Dubossarsky (project leader, chief designer), Yuriy Tyshetskiy (design, implementation, testing)
Maintainer
Yuriy Tyshetskiy
Description
Implementation of the sparse autoencoder in R environment, following the notes of Andrew Ng (http://www.stanford.edu/class/archive/cs/cs294a/cs294a.1104/sparseAutoencoder.pdf). The features learned by the hidden layer of the autoencoder (through unsupervised learning of unlabeled data) can be used in constructing deep belief neural networks.
License
GPL-2
NeedsCompilation
no
Packaged
2015-07-02 05:28:56 UTC; ytyshetskiy
Repository
CRAN
Date/Publication
2015-07-02 09:09:12

install.packages('autoencoder')

1.1

03 years ago

Yuriy Tyshetskiy

GPL-2

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