Package: deepNN 1.2

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.

Authors:Benjamin Taylor [aut, cre]

deepNN_1.2.tar.gz
deepNN_1.2.zip(r-4.7)deepNN_1.2.zip(r-4.6)deepNN_1.2.zip(r-4.5)
deepNN_1.2.tgz(r-4.6-any)deepNN_1.2.tgz(r-4.5-any)
deepNN_1.2.tar.gz(r-4.7-any)deepNN_1.2.tar.gz(r-4.6-any)
deepNN_1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
deepNN/json (API)

# Install 'deepNN' in R:
install.packages('deepNN', repos = c('https://bentaylor1.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.34 score 22 scripts 229 downloads 22 exports 2 dependencies

Last updated from:424b1fb71c. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE127
source / vignettesOK132
linux-release-x86_64NOTE114
macos-release-arm64NOTE99
macos-oldrel-arm64NOTE103
windows-develNOTE76
windows-releaseNOTE88
windows-oldrelNOTE85
wasm-releaseOK97

Exports:download_mnistdropoutProbshyptanidentL1_regularisationL2_regularisationlogisticmultinomialnbiasparnetworknnetparNNgrad_testNNpredictNNpredict.regressionno_regularisationQlossReLUsmoothReLUsoftmaxtrainwmultinomialwQloss

Dependencies:latticeMatrix