What does "deep learning" utilize to model complex data patterns?

Deep learning utilizes neural networks with multiple layers to model complex data patterns. This approach is designed to mimic the way the human brain processes information, allowing it to learn from vast amounts of data. Each layer in a deep learning model captures different levels of abstraction, enabling the system to recognize intricate patterns and relationships within the data.

By stacking multiple layers, deep learning models can handle highly non-linear data and perform tasks such as image recognition, natural language processing, and more effectively than traditional machine learning techniques. The depth of these networks plays a crucial role in their ability to generalize and make predictions based on the underlying patterns they have learned during training. This hierarchical learning process is what distinguishes deep learning from other methods that utilize simpler structures.