Input-process-output (I-P-O) is a structured methodology for capturing and visualizing all of the inputs, outputs, and process steps that are required to transform inputs into outputs.

This reversing process is known as backpropagation and is a main feature of machine learning in general. An enormous amount of variety is encompassed within the basic structure of a neural network.

If you examine both figures you'll see that, in essence, a neural network accepts some numeric inputs (2.0, 3.0 and 4.0 in this example), does some processing and produces some numeric outputs (0.93 ...

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We generally think of plugging new input/output devices into a computer and turning it on to get something new. But with this model, the previously stored data is changed by the new parts, too.