Multi-layer perceptron (MLP): Difference between revisions
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== How does it work or a deeper look == | == How does it work or a deeper look == | ||
* | * 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.) | |||
== Examples == | == Examples == | ||
Revision as of 21:44, 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.)
Examples[edit]
Please include some example of how your concept is actually used. Your example must include WHERE it is used, and WHAT IS BENEFIT of it being used.
Pictures, diagrams[edit]
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:
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