Multiple layers in neural networks refer to the arrangement of interconnected neurons organized into distinct levels, each serving a specific function in processing data. The simplest form, known as a multilayer perceptron (MLP), consists of an input layer, one or more hidden layers, and an output layer. Each layer transforms the input data through weighted connections and activation functions, allowing the network to learn complex patterns. This layered architecture enhances the model’s ability to capture intricate relationships within the data, making it a powerful tool for tasks such as classification, regression, and more in various domains, including computer vision and natural language processing.