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) ) and "Reducing the dimensionality of data with neural networks" (G. E. Hinton, R. R. Salakhutdinov (2006) ). This method includes a pre training with the contrastive divergence method published by G.E Hinton (2002) 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.

Documentation

Manual: darch.pdf
Vignette: None available.

Maintainer: Martin Drees <mdrees at stud.fh-dortmund.de>

Author(s): Martin Drees*, Johannes Rueckert*, Christoph M. Friedrich*, Geoffrey Hinton*, Ruslan Salakhutdinov*, Carl Edward Rasmussen*,

Install package and any missing dependencies by running this line in your R console:

install.packages("darch")

Depends R (>= 3.0.0)
Imports stats, methods, ggplot2, reshape2, futile.logger(>=1.4.1), caret, Rcpp(>=0.12.3)
Suggests foreach, doRNG, NeuralNetTools, gputools, testthat, plyr(>=1.8.3.9000)
Enhances
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Package darch
Materials
URL https://github.com/maddin79/darch
Task Views MachineLearning
Version 0.12.0
Published 2016-07-20
License GPL (>= 2) | file LICENSE
BugReports https://github.com/maddin79/darch/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks darch check results
Package source darch_0.12.0.tar.gz