Hidden Layer

Hidden layers receive information from the previous layer, transform it using weights, biases, and activation functions, and pass the resulting information to the next layer. These layers are called “hidden” because their values are internal to the network rather than directly provided as inputs or observed as final outputs. Deep neural networks typically contain multiple hidden layers.

Hidden layers enable neural networks to learn complex patterns and representations from data. Earlier layers may learn relatively simple features, while deeper layers can combine them into more abstract representations. The number and structure of hidden layers can significantly influence a model’s learning capacity, computational requirements, and performance.