The Multi-Layer Perceptron (MLP) is the foundation of modern neural networks — the model that gave rise to deep learning itself. In this complete guide, we break down the architecture, intuition, and mathematics behind MLPs. You’ll learn how multiple perceptrons, when stacked in layers with activation functions, can model complex non-linear relationships and make intelligent predictions.

The Perceptron is one of the simplest yet most important algorithms in supervised learning. Acting as the foundation for modern neural networks, it uses inputs, weights, and an activation function to make binary predictions. In this guide, we explore how the Perceptron learns, interprets weights, and forms decision boundaries — along with its biggest limitation: linear separability.