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Percettrone
The perceptron is the most elementary mathematical model of an artificial neuron: it receives a set of numerical inputs, combines them by computing a weighted sum, and applies a function to this result that decides the final output, originally often a simple threshold producing a binary response. It is considered the basic building block from which modern neural networks conceptually derive.
Each input to the perceptron is multiplied by an associated weight, the products are summed together with a bias term, and the result passes through an activation function that determines the output. In its original form, a single perceptron can learn to separate linearly separable data, meaning data that can be divided into two categories by a line or a plane, but it cannot solve problems where this linear separation is not possible.
A single perceptron today has mostly educational and historical value, but the principle it embodies, a weighted combination of inputs followed by an activation function, is exactly the basic mechanism of every single artificial neuron present in the layers of modern deep neural networks, including those used in language models. By stacking and connecting many perceptrons across multiple layers, the multilayer perceptron overcomes the limit of linear separability.
The term was coined to describe this first model of an artificial neuron, loosely inspired by the functioning of biological neurons, and it dates back to the earliest studies on artificial neural networks conducted in the 1950s.
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