Beginner · Neural Networks
Multilayer perceptron (MLP)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A feedforward neural network with multiple layers of fully connected neurons and nonlinear activation functions.
Technical Definition
A feedforward neural network with multiple layers of fully connected neurons and nonlinear activation functions.
How it works
A multilayer perceptron (MLP) is a type of artificial neural network where data flows in one direction, from input to output, through multiple layers. Each layer contains neurons that are fully connected to the neurons in the next layer. These networks use nonlinear activation functions, allowing them to learn complex patterns and distinguish data that is not linearly separable.
Related Concepts
- Neural Network — A computing system inspired by biological neural networks that learns patterns from data through interconnected layers of nodes.
- Activation Function — A non-linear function applied to a neuron's output, enabling the network to learn complex, non-linear relationships.
- Feedforward neural network (FFN) — A feedforward neural network is a type of artificial neural network where connections between nodes do not form cycles; information moves in only one direction.