Rosenblatt Perceptron

The Rosenblatt Perceptron is a basic type of artificial neural network designed to simulate decision-making, similar to a simple brain cell. It takes in multiple inputs, applies learned weights to them, adds these together, and then uses a threshold to decide whether to activate or not. Essentially, it can learn to distinguish between different categories—like telling apart emails that are spam from those that are not—by adjusting the weights based on its mistakes. This process of learning makes the perceptron a foundational model for more complex machine learning systems.