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deepNN
View on CRAN: Click
here
Download and install deepNN package within the R console
Install from CRAN:
install.packages("deepNN")
Install from Github:
library("remotes")
install_github("cran/deepNN")
Install by package version:
library("remotes")
install_version("deepNN", "1.2")
Attach the package and use:
library("deepNN")
Maintained by
Benjamin Taylor
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-03-08
Latest Update: 2023-08-25
Description:
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.
How to cite:
Benjamin Taylor (2019). deepNN: Deep Learning. R package version 1.2, https://cran.r-project.org/web/packages/deepNN. Accessed 21 Feb. 2025.
Previous versions and publish date:
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Complete documentation for deepNN
Functions, R codes and Examples using
the deepNN R package
Some associated functions: L1_regularisation . L2_regularisation . MLP_net . NNgrad_test . NNpredict . NNpredict.regression . Qloss . ReLU . addGrad . addList . backprop_evaluate . backpropagation_MLP . bias2list . biasInit . deepNN-package . download_mnist . dropoutProbs . gradInit . hyptan . ident . logistic . memInit . multinomial . nbiaspar . network . nnetpar . no_regularisation . smoothReLU . softmax . stopping.default . stopping.maxit . stopping . stopping.revdir . train . updateStopping.classification . updateStopping . updateStopping.regression . wQloss . weights2list . wmultinomial .
Some associated R codes: MLP.R . activations.R . convolution.R . cost_functions.R . deepNN.R . grad_test.R . miscellaneous.R . networks.R . optimisation.R . prediction.R . regularisation.R . stopping_criteria.R . Full deepNN package functions and examples
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