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
DESCRIPTION
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.

1.36 score 23 scripts 202 downloads 22 exports 2 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE120
source / vignettesOK140
linux-release-x86_64NOTE112
macos-release-arm64NOTE93
macos-oldrel-arm64NOTE106
windows-develNOTE82
windows-releaseNOTE83
windows-oldrelNOTE119
wasm-releaseOK90

Exports:download_mnistdropoutProbshyptanidentL1_regularisationL2_regularisationlogisticmultinomialnbiasparnetworknnetparNNgrad_testNNpredictNNpredict.regressionno_regularisationQlossReLUsmoothReLUsoftmaxtrainwmultinomialwQloss

Dependencies:latticeMatrix