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.

Version:	1.1
Published:	2015-07-02
Author:	Eugene Dubossarsky (project leader, chief designer), Yuriy Tyshetskiy (design, implementation, testing)
Maintainer:	Yuriy Tyshetskiy <yuriy.tyshetskiy at nicta.com.au>
License:	GPL-2
NeedsCompilation:	no
CRAN checks:	autoencoder results

Documentation:

Reference manual:

autoencoder.pdf

Downloads:

Package source:	autoencoder_1.1.tar.gz
Windows binaries:	r-devel: autoencoder_1.1.zip, r-release: autoencoder_1.1.zip, r-oldrel: autoencoder_1.1.zip
macOS binaries:	r-release (arm64): autoencoder_1.1.tgz, r-oldrel (arm64): autoencoder_1.1.tgz, r-release (x86_64): autoencoder_1.1.tgz, r-oldrel (x86_64): autoencoder_1.1.tgz
Old sources:	autoencoder archive

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