What key component distinguishes deep learning from traditional machine learning?

Deep learning is primarily characterized by its architecture, where the presence of multiple layers in neural networks enables the model to learn hierarchical representations of data. Each layer in a deep network processes information at increasing levels of abstraction, allowing the model to capture complex patterns and relationships in the data that traditional machine learning algorithms, which often use simpler structures or fewer layers, cannot effectively model.

This ability to stack layers and create deeper networks is foundational to the success of deep learning across various applications, such as image recognition and natural language processing. Unlike traditional machine learning, where models might have only one or two layers, deep learning leverages many layers to perform feature extraction and transformation of inputs, resulting in enhanced performance on complex tasks.