Multi-layer perceptron (MLP)

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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.)

External links[edit]

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References[edit]

↑ http://www.flaticon.com/

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

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