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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.

Functions (40)

L1_regularisation

L1_regularisation function

L2_regularisation

L2_regularisation function

MLP_net function

NNgrad_test

NNgrad_test function

NNpredict function

NNpredict.regression

NNpredict.regression function

Qloss function

ReLU

ReLU function

addGrad function

addList function

backprop_evaluate

backprop_evaluate function

backpropagation_MLP

backpropagation_MLP function

bias2list function

biasInit function

deepNN-package

deepNN

download_mnist

download_mnist function

dropoutProbs

dropoutProbs function

gradInit function

hyptan function

ident function

logistic function

memInit function

multinomial

multinomial function

nbiaspar function

network function

nnetpar function

no_regularisation

no_regularisation function

smoothReLU function

softmax function

stopping function

stopping.default

stopping.default function

stopping.maxit

stopping.maxit function

stopping.revdir

stopping.revdir function

train function

updateStopping

updateStopping function

updateStopping.classification

updateStopping.classification function

updateStopping.regression

updateStopping.regression function

wQloss function

weights2list

weights2list function

wmultinomial

wmultinomial function

deepNN

Deep Learning

v1.0

GPL-3

Authors

Benjamin Taylor [aut, cre]

Initial release

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