Multiple layers in neural networks refer to the arrangement of interconnected neurons organized into distinct layers, each performing specific functions. In a typical architecture, there are input, hidden, and output layers. The input layer receives data, hidden layers process this information through various transformations, and the output layer produces the final result. This multi-layer structure allows neural networks to learn complex patterns and representations from data, enhancing their ability to perform tasks such as classification and regression. The depth and configuration of these layers significantly influence the model’s performance and capacity to generalize from training data.