What is deep learning?

Deep learning is indeed a type of machine learning that utilizes deep neural networks, which distinguishes it from traditional machine learning techniques. Deep neural networks are composed of multiple layers of interconnected nodes or "neurons," which allow for the modeling of complex patterns and representations in data. This capability enables deep learning to effectively process large volumes of data and perform tasks such as image and speech recognition, natural language processing, and more.

The reason for this classification lies in the architecture of deep learning models. The "deep" in deep learning refers to the presence of multiple layers in the neural network. Each layer extracts progressively higher-level features, leading to advanced capability in identifying intricate patterns that simpler algorithms may not be able to capture.

In contrast to some of the other options, deep learning does not inherently require manual data labeling exclusively, nor is it characterized by simple algorithms. While labeled data may be commonly used in many deep learning applications, such as image classification, the field also explores unsupervised and semi-supervised learning techniques where labeling is minimal or absent. Additionally, while it is indeed a subset of machine learning, saying it relies solely on simple algorithms would misrepresent its complexity and methodology.