Which of the following best describes Deep Learning?

Deep Learning is best described as a subfield using artificial neural networks. This approach involves algorithms inspired by the human brain's structure and function, enabling the system to learn from large amounts of data. Deep learning models are capable of identifying patterns and making predictions with significant accuracy, especially in handling complex tasks such as image and speech recognition.

The focus on artificial neural networks distinguishes deep learning from conventional machine learning techniques, which often rely on simpler models and algorithms that do not employ the same level of abstraction and complexity available in neural networks. This advanced capability to process data through multiple layers of neurons allows deep learning systems to capture intricate relationships and hierarchical representations.

Other options do not accurately represent the essence of deep learning. For instance, while some might view it as a simplification of data, deep learning actually involves complex models that require substantial data for effective training. Additionally, it is not a framework for rule-based systems, as rule-based systems operate on predefined rules rather than learning from data as deep learning does. Therefore, the characterization of deep learning as a subfield using artificial neural networks is the most accurate.