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deepNN: Deep Learning

Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning.

Version: 1.2
Depends: R (≥ 3.2.1)
Imports: stats, graphics, utils, Matrix, methods
Published: 2023-08-25
DOI: 10.32614/CRAN.package.deepNN
Author: Benjamin Taylor [aut, cre]
Maintainer: Benjamin Taylor <benjamin.taylor.software at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: deepNN results

Documentation:

Reference manual: deepNN.pdf

Downloads:

Package source: deepNN_1.2.tar.gz
Windows binaries: r-devel: deepNN_1.2.zip, r-release: deepNN_1.2.zip, r-oldrel: deepNN_1.2.zip
macOS binaries: r-release (arm64): deepNN_1.2.tgz, r-oldrel (arm64): deepNN_1.2.tgz, r-release (x86_64): deepNN_1.2.tgz, r-oldrel (x86_64): deepNN_1.2.tgz
Old sources: deepNN archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=deepNN to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.