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) <> Neural Networks YouTube playlist. Michael A. Nielsen <> 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 < at>
License: GPL-3
NeedsCompilation: no
CRAN checks: deepNN results


Reference manual: deepNN.pdf


Package source: deepNN_1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


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