Deep learning is accurately defined as a subset of machine learning that leverages neural networks with many layers to analyze a variety of factors in data. This structure allows deep learning models to process large volumes of data and learn complex patterns and features at multiple levels of abstraction. The multi-layer architecture, often referred to as deep neural networks, enables the model to extract intricate representations from raw data, making it highly effective for tasks like image recognition, natural language processing, and more.
The depth of these networks, which can consist of many hidden layers, is what sets deep learning apart from traditional machine learning approaches that typically utilize simpler models. By stacking layers of neurons, each layer can learn to recognize features or patterns that the previous layer has detected, leading to improved performance on complex tasks. This is why deep learning has become a cornerstone of modern artificial intelligence, outperforming other methods in many applications.