What type of architecture does a deep learning model usually use?

A deep learning model typically utilizes a neural network with multiple layers, which is fundamental to its architecture. This type of architecture allows deep learning models to learn complex patterns and representations from large amounts of data. The multiple layers in a neural network enable the model to process information in stages, where each layer extracts higher-level features from the raw input data. For example, in image processing tasks, lower layers might identify edges and textures, while deeper layers can recognize more abstract shapes or entire objects.

The depth of the neural network—referring to the number of layers—affords these models their ability to handle tasks such as image recognition, natural language processing, and more, where traditional models may fall short due to their more simplified architectures. This is in stark contrast to the alternatives, which either lack the capacity or complexity that deep learning requires.