Which architecture is typically associated with deep learning models?

Deep learning models are primarily based on neural networks, which are designed to simulate the way human brains operate. These networks are composed of layers of interconnected nodes or "neurons," allowing them to learn complex patterns from large amounts of data through processes such as backpropagation and gradient descent.

Neural networks can capture intricate relationships in data due to their multi-layered structure, enabling them to perform tasks such as image recognition, natural language processing, and more, which are characteristic of deep learning applications. The depth of these networks—reflected in the number of layers—allows for hierarchical feature extraction and represents a significant advancement over traditional machine learning techniques.

In contrast, the other architectures listed, such as decision trees, support vector machines, and linear regression, do not embody the multi-layered, interconnected structure that defines deep learning. They are typically more straightforward models with limited capacity for handling high-dimensional data and complex data representations.