In the context of neural networks, multiple layers refer to the arrangement of several layers of neurons that work together to process inputs and generate outputs. Each layer consists of multiple neurons, and these layers can be stacked to form deep learning models, such as Multi-Layer Perceptrons (MLPs) or Convolutional Neural Networks (CNNs).

Layers in a neural network take a set of inputs, generate corresponding outputs, and are characterized by a set of tunable parameters. The entire model, as well as its individual layers, share this structure, allowing for complex transformations of the input data as it passes through the network ².

Moreover, in advanced architectures like ResNet, layers are often organized into repeating patterns or modules, which simplifies the implementation of complex networks. These modules can represent a single layer, a group of layers, or even the entire model, facilitating the design of sophisticated architectures for various tasks in computer vision and natural language processing ².

Overall, the use of multiple layers enables neural networks to learn hierarchical representations of data, improving their ability to perform complex tasks.