Multiple layers in neural networks refer to the arrangement of interconnected neurons organized into distinct layers, each serving a specific function in processing data. The simplest form, known as a single-layer perceptron, consists of an input layer and an output layer. However, more complex architectures, such as multilayer perceptrons (MLPs), incorporate one or more hidden layers between the input and output layers. These hidden layers enable the network to learn intricate patterns and representations from the data, enhancing its ability to perform tasks like classification and regression. The depth and configuration of these layers significantly influence the model’s performance and capacity to generalize.