Multi-layer perceptron (MLP): Difference between revisions

Revision as of 21:47, 6 April 2018

This is student work which has not yet been approved as correct by the instructor

Case study notes^[1]

Introduction[edit]

Multi-layer perceptrons are simply a type of neural network consisting of at least 3 nodes. The input nodes, the hidden nodes, and the output nodes. Each node is a neuron that 'activates' and turns on the next node etc.

 <ref> https://en.wikipedia.org/wiki/Multilayer_perceptron</ref>

How does it work or a deeper look[edit]

Multi-layer perceptrons use backpropagation as part of their learning phase.
The nodes use a non-linear activation function (Basically they turn each other on)
MLP's are fully connected (each hidden node is connected to each input node etc.)

Pictures, diagrams[edit]

External links[edit]

It would be helpful
to include many links
to other internet resources
to help fellow students
Please make sure the content is good
and don't link to a google search results, please

References[edit]

↑ http://www.flaticon.com/

[1] ttp://www.flaticon.com/

[1]

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 == Pictures, diagrams ==
-Pictures and diagrams go a LONG way to helping someone understand a topic. Especially if your topic is a little abstract or complex. Using a picture or diagram is a two part process:
+[[File:Mlp-network.png|thumb]]
-# [https://www.mediawiki.org/wiki/Help:Managing_files upload a file]
-# [https://www.mediawiki.org/wiki/Help:Images use the file on a wiki page]
 == External links ==