A perceptron is one of the simplest forms of artificial neural networks. It’s a binary classifier that takes multiple binary inputs and produces a single binary output.
Here’s how it works:
- Inputs: Each input is associated with a weight. These inputs could be binary (0 or 1) or real-valued numbers.
- Weights: Each input is multiplied by a weight. The perceptron learns the appropriate weights during training to make accurate predictions.
- Summation: The weighted inputs are summed together with a bias term.
- Activation: The sum is passed through an activation function. Traditionally, this activation function is a step function. For example, if the sum is above a certain threshold, the perceptron outputs 1; otherwise, it outputs 0.
- Output: The output of the activation function is the output of the perceptron.
