What technology is characterized by its ability to learn patterns autonomously, such as distinguishing between objects like cats and dogs?

Deep learning is characterized by its ability to learn patterns autonomously through the use of artificial neural networks with multiple layers, often referred to as deep neural networks. This technology excels in complex tasks like image and speech recognition, where it automatically identifies features and patterns directly from the raw data. In the context of distinguishing between objects such as cats and dogs, deep learning algorithms can take images as input and learn to differentiate between the two by analyzing various features, including shapes, colors, and textures, without requiring manual feature extraction.

The other options, while related to pattern recognition and data analysis, do not encompass the autonomous learning capabilities of deep learning to the same extent. Basic pattern recognition often involves simpler techniques that may require more explicit programming of rules, and traditional neural networks may not delve as deeply or effectively into pattern learning as modern deep learning techniques. Linear algebra transformations are fundamental mathematical concepts used in many areas of data analysis, including some machine learning methods, but they do not directly characterize an autonomous learning system like deep learning does.