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: 0.3
Depends: R (≥ 3.2.1)
Imports: stats, graphics, utils, Matrix, methods
Published: 2019-03-08
Author: Benjamin Taylor [aut, cre]
Maintainer: Benjamin Taylor <b.taylor1 at lancaster.ac.uk>
License: GPL-3
NeedsCompilation: no
CRAN checks: deepNN results

Downloads:

Reference manual: deepNN.pdf
Package source: deepNN_0.3.tar.gz
Windows binaries: r-devel: deepNN_0.3.zip, r-devel-gcc8: deepNN_0.3.zip, r-release: deepNN_0.3.zip, r-oldrel: deepNN_0.3.zip
OS X binaries: r-release: deepNN_0.3.tgz, r-oldrel: deepNN_0.3.tgz

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