A Multilayer Perceptron (MLP) is a type of artificial neural network that is widely used in machine learning for solving problems related to classification and regression. It consists of multiple layers of nodes (neurons), including:
Input layer: Receives the input features.
One or more hidden layers: Perform nonlinear transformations and extract complex features.
Output layer: Produces the final prediction or classification.
Each neuron in one layer is connected to every neuron in the next layer through weighted connections. MLP uses a process called backpropagation for training, which adjusts these weights to minimize the error between the predicted output and the actual target.
Because of its layered structure and nonlinear activation functions, MLP can model complex relationships in data, making it suitable for a wide range of applications like image recognition, speech processing, and time series prediction.