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How Does The Single Layer Perceptron Model Works

This article explains how a perceptron works.

How Does The Perceptron Work?

A perceptron is a single-layer neural network.

There are three components of a perceptron: -

When the input reaches the hidden layer, weights are applied to each input.

There are two parts of a hidden layer. In the first part, we compute the weighted sum of inputs and then add a bias.

The second part is an activation function that takes z and produces the output.

In a perceptron, when the prediction error (loss) is large, the weights are updated during training to minimize the loss.

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