How does deep learning differ from traditional machine learning?

Deep learning is distinguished from traditional machine learning primarily through its use of neural networks that contain multiple layers, which allows it to model complex patterns in data. This layered architecture, often referred to as deep neural networks, enables the system to learn representations of data at various levels of abstraction. For example, in image recognition, the first layers may identify simple features like edges, while deeper layers can recognize more complex shapes or entire objects.

This multi-layered approach allows deep learning to automatically extract relevant features from raw data without the need for extensive manual feature engineering, which is often required in traditional machine learning methods. As a result, deep learning can excel in tasks with large amounts of unstructured data, such as images, audio, and text.

The other choices either misrepresent the nature of deep learning or incorrectly pertain to how traditional machine learning functions. By focusing on deep neural networks and their capacity to handle intricate patterns in large datasets, the correct choice highlights a fundamental shift in methodology that characterizes deep learning.