Hidden layers are the processing layers inside a neural network that sit between the input layer—where data enters—and the output layer—where results emerge. They are where the actual learning happens, transforming raw input into meaningful output through a series of increasingly refined calculations.
A neural network without hidden layers would simply echo its input back as output. As data passes through each hidden layer, the network builds a progressively richer understanding of it, detecting patterns and extracting features at each stage. This layered processing is what gives neural networks their ability to handle complex tasks.
Stacking multiple hidden layers is also the basis of deep learning—the more hidden layers a network contains, the deeper it is considered to be.
Each hidden layer is made up of units called nodes or neurons. Every node performs a simple operation on the data it receives—detecting an edge in an image, registering a particular value, or completing a calculation—then passes the result to the nodes in the next layer. Because each node in one layer connects to each node in the next, information compounds as it moves through the network, allowing the model to recognize increasingly abstract and complex relationships.