Which of the following best describes deep learning?

Deep learning is characterized by its utilization of neural networks that consist of multiple layers, commonly referred to as deep neural networks. This architecture enables the model to learn complex representations of data by processing it through several layers, each extracting different levels of abstraction. The basic idea is that lower layers can learn simple features, while higher layers can capture more intricate patterns and relationships.

This multi-layer approach is what allows deep learning to excel in tasks such as image and speech recognition, natural language processing, and other applications where data is vast and complicated. It provides a means of automating the feature extraction process, making it distinct from traditional machine learning methods that often require manual feature selection.

The other options do not capture the essence of deep learning effectively. A simple algorithm merely refers to basic classification tasks and lacks the complexity associated with deep learning. Shallow networks do not leverage the advantages of depth in structure, and methods for performing basic arithmetic operations are unrelated to the learning capabilities and architectures used in deep learning.