AI Glossary
Hidden Layer
A layer within a neural network that processes information between the input and output layers.
Overview
When information enters a neural network, it does not move directly from input to output.
Instead, it often passes through one or more hidden layers.
A hidden layer is a layer of artificial neurons located between the input layer and output layer.
These layers perform calculations and transformations that help the network identify patterns within data.
The term “hidden” does not mean secret.
It simply means that the calculations occurring within these layers are not directly visible as inputs or outputs.
As information passes through hidden layers, the network gradually extracts more meaningful patterns.
Simple neural networks may contain only one hidden layer.
Modern deep learning systems can contain dozens or even hundreds of hidden layers.
Understanding hidden layers helps explain how neural networks learn increasingly complex representations of information.
Why It Matters
Hidden layers enable neural networks to learn patterns that would be difficult to capture using simpler models.
Real-World Example
A facial recognition system may use hidden layers to identify shapes, edges, facial features, and complex visual patterns.
Related Concepts
- Artificial Neuron
- Input Layer
- Output Layer
- Neural Network
- Deep Learning