A Multilayer Perceptron (MLP) is a type of artificial neural network composed of an input layer, one or more hidden layers, and an output layer. Each layer consists of neurons (nodes) that are fully connected to the neurons in the next layer.
Key Features:
Feedforward structure: Data moves in one direction—from input to output.
Nonlinear activation functions: Such as ReLU, sigmoid, or tanh, enabling the network to learn complex patterns.
Supervised learning: Commonly trained using backpropagation and gradient descent.
Purpose:
MLPs are used for tasks like:
Classification (e.g., spam detection)
Regression (e.g., predicting house prices)
Function approximation
Pattern recognition
