backpropagation_MLP

A function to perform backpropagation for a multilayer perceptron.

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.

Benjamin Taylor

deepNN

Deep Learning

backpropagation_MLP function

<dl><dt>MLPNet</dt>
<dd>output from the function MLP_net, as applied to some data with given parameters</dd>
<dt>loss</dt>
<dd>the loss function, see ?Qloss and ?multinomial</dd>
<dt>truth</dt>
<dd>the truth, a list of vectors to compare with output from the feed-forward network</dd></dl>

Arguments

backpropagation_MLP function — backpropagation_MLP

<dl>

<dt>MLPNet</dt>
<dd>output from the function MLP_net, as applied to some data with given parameters</dd>


<dt>loss</dt>
<dd>the loss function, see ?Qloss and ?multinomial</dd>


<dt>truth</dt>
<dd>the truth, a list of vectors to compare with output from the feed-forward network</dd>

</dl>

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backpropagation_MLP: backpropagation_MLP function

Description

Usage

Value

Arguments

References

See Also