Transfer Learning

Topology in artificial intelligence explores the fundamental geometric properties of data that remain unchanged under transformations like stretching or bending. It helps in understanding the shape and structure of complex data sets, aiding in manifold learning, dimensionality reduction, and pattern recognition. This field provides insights into the intrinsic features of data, enhancing algorithms' ability to model and analyze high-dimensional information.