darch: Package for Deep Architectures and Restricted Boltzmann Machines

The darch package is built on the basis of the code from G. E. Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief nets). This package is for generating neural networks with many layers (deep architectures) and train them with the method introduced by the publications "A fast learning algorithm for deep belief nets" (G. E. Hinton, S. Osindero, Y. W. Teh (2006) <doi:10.1162/neco.2006.18.7.1527>) and "Reducing the dimensionality of data with neural networks" (G. E. Hinton, R. R. Salakhutdinov (2006) <doi:10.1126/science.1127647>). This method includes a pre training with the contrastive divergence method published by G.E Hinton (2002) <doi:10.1162/089976602760128018> and a fine tuning with common known training algorithms like backpropagation or conjugate gradients. Additionally, supervised fine-tuning can be enhanced with maxout and dropout, two recently developed techniques to improve fine-tuning for deep learning.

Version: 0.12.0
Depends: R (≥ 3.0.0)
Imports: stats, methods, ggplot2, reshape2, futile.logger (≥ 1.4.1), caret, Rcpp (≥ 0.12.3)
LinkingTo: Rcpp
Suggests: foreach, doRNG, NeuralNetTools, gputools, testthat, plyr (≥
Published: 2016-07-20
Author: Martin Drees [aut, cre, cph], Johannes Rueckert [ctb], Christoph M. Friedrich [ctb], Geoffrey Hinton [cph], Ruslan Salakhutdinov [cph], Carl Edward Rasmussen [cph],
Maintainer: Martin Drees <mdrees at stud.fh-dortmund.de>
BugReports: https://github.com/maddin79/darch/issues
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
URL: https://github.com/maddin79/darch
NeedsCompilation: yes
Citation: darch citation info
Materials: README
CRAN checks: darch results


Reference manual: darch.pdf
Package source: darch_0.12.0.tar.gz
Windows binaries: r-devel: darch_0.12.0.zip, r-release: darch_0.12.0.zip, r-oldrel: darch_0.12.0.zip
OS X Mavericks binaries: r-release: darch_0.12.0.tgz, r-oldrel: darch_0.12.0.tgz
Old sources: darch archive

Reverse dependencies:

Reverse depends: deeplearning


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