07-28-2023-1159 - PERCEPTRON (DRAFT)

Mark I Perceptron machine, the first implementation of the perceptron algorithm. It was connected to a camera with 20×20 cadmium sulfide photocells to make a 400-pixel image. The main visible feature is a patch panel that set different combinations of input features. To the right, arrays of potentiometers that implemented the adaptive weights.^[2]: 213

https://en.wikipedia.org/wiki/Perceptron

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"Perceptrons" redirects here. For the 1969 book, see Perceptrons (book).

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In machine learning, the perceptron (or McCulloch-Pitts neuron) is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class.^[1] It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. 

https://en.wikipedia.org/wiki/Perceptron