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AI•Machine Learning

Perceptrons

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Perceptrons

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A

Perceptron is an

Artificial Neuron.

It is the simplest possible

Neural Network.

Neural Networks are the building blocks of

Machine Learning.

Frank Rosenblatt

Frank Rosenblatt

(1928 – 1971) was an American psychologist notable in the field of Artificial Intelligence. In 1957 he started something really big. He "invented" a Perceptron program, on an IBM 704 computer at Cornell Aeronautical Laboratory. Scientists had discovered that brain cells (

Neurons

) receive input from our senses by electrical signals. The Neurons, then again, use electrical signals to store information, and to make decisions based on previous input.

Frank had the idea that

Perceptrons could simulate brain principles, with the ability to learn and make decisions.

The Perceptron

The original

Perceptron was designed to take a number of binary inputs, and produce one binary output (0 or 1). The idea was to use different weights to represent the importance of each input, and that the sum of the values should be greater than a threshold value before making a decision like yes or no (true or false) (0 or 1).

Perceptron Example

Imagine a perceptron (in your brain). The perceptron tries to decide if you should go to a concert. Is the artist good? Is the weather good?

What weights should these facts have?

Criteria

Input

Weight

Artists is Good x1

= 0 or 1 w1 = 0.7

Weather is Good x2

= 0 or 1 w2 = 0.6

Friend will Come x3

= 0 or 1 w3 = 0.5

Food is Served x4

= 0 or 1 w4 = 0.3

Alcohol is Served x5

= 0 or 1 w5 = 0.4

The Perceptron Algorithm

Frank Rosenblatt suggested this algorithm:

Set a threshold value

Multiply all inputs with its weights

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