1: My First Perceptron with Python: Analyzed and Explained with a Practical Sense (Tutorial Series: Programming Artificial Neural Networks Step by Step with Python) by Eric Joel Barragán González

Author:Eric Joel Barragán González [Barragán González, Eric Joel]
Language: eng
Format: epub
Publisher: UNKNOWN
Published: 2017-02-05T08:00:00+00:00

Our algorithm works with a Correction of Errors, since every time you commit an Error, action is taken to seek to correct it.

As for the algorithm, the supervision is implicit, so to distinguish it at the beginning is not obvious. I hope that with the previous explanations you understand it, and that in (Point 13), where in a function I completely eliminate supervision, I just made it clear.

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7.2 Type of Association between Inputs and Outputs

In contrast to ours, there are the auto-associative, in which their algorithm when performing the calculations, takes each combination of Inputs, to the Output with which they have more in common. In our case the Perceptron is Heteroasciative, in this the association is not free, but predetermined, so it requires supervision and feedback, so that the particular combination of Inputs, Learn to Associate to a particular Output.

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