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.)
Pictures, diagrams[edit]
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External links[edit]
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