What approach does Deep Learning use in machine learning?

Deep Learning makes extensive use of neural networks to tackle intricate tasks, which is why the correct answer is based on this approach. Neural networks are designed to simulate the way human brains operate, allowing them to process vast amounts of data and identify intricate patterns. This enables Deep Learning models to perform with high accuracy on complex tasks such as image and speech recognition, natural language processing, and even strategic game playing.

The architecture of neural networks, often involving multiple layers (hence the term "deep"), allows them to learn hierarchical representations of data. For instance, in image processing, early layers might detect edges, while deeper layers could recognize shapes or even objects. The ability of these models to learn from unstructured data and generalize from examples without explicit programming is a cornerstone of Deep Learning’s success in various applications.

In contrast, the other approaches mentioned, such as decision trees, linear regression, and support vector machines, are effective for specific types of data patterns and tasks but do not have the same capacity for handling the complexity and volume of data managed by deep learning neural networks. Thus, while they are valuable tools in the machine learning toolbox, they do not embody the distinctive characteristics and capabilities of Deep Learning as neural networks do.