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: | 1.0 |
| Depends: | R (≥ 3.2.1) |
| Imports: | stats, graphics, utils, Matrix, methods |
| Published: | 2020-03-05 |
| Author: | Benjamin Taylor [aut, cre] |
| Maintainer: | Benjamin Taylor <benjamin.taylor.software at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | deepNN results |
| Reference manual: | deepNN.pdf |
| Package source: | deepNN_1.0.tar.gz |
| Windows binaries: | r-devel: deepNN_1.0.zip, r-release: deepNN_1.0.zip, r-oldrel: deepNN_1.0.zip |
| macOS binaries: | r-release: deepNN_1.0.tgz, r-oldrel: deepNN_1.0.tgz |
| Old sources: | deepNN archive |
Please use the canonical form https://CRAN.R-project.org/package=deepNN to link to this page.