In neural networks, multiple layers are essential for building complex models that can learn intricate patterns from data. Each layer consists of a set of neurons that take inputs, generate outputs, and have associated parameters that can be tuned during training. The structure of a neural network can be thought of as a series of these layers stacked together, where the output of one layer serves as the input to the next.

For instance, in a Multi-Layer Perceptron (MLP), both the entire model and its individual layers share a similar structure. The model takes raw inputs, generates predictions, and has parameters that are updated based on the learning process. This layered approach allows for the abstraction of features at different levels, enabling the model to learn from simple to complex representations of the data ².

Moreover, in more advanced architectures like ResNet, layers are organized into repeating patterns, which simplifies the implementation of deep networks. This modular approach allows for greater flexibility and efficiency in designing neural networks ².