SVD

In machine learning, SVD is short for Singular Value Decomposition. It is a mathematical method that is used to decompose a matrix into its constituent parts. In particular, it decomposes a matrix into three matrices: a left singular matrix, a diagonal matrix, and a right singular matrix. These matrices can then be used for various purposes, such as dimensionality reduction, data compression, and data analysis. SVD is often used in natural language processing and recommender systems, where it can help to extract meaningful information from large and complex datasets. It is also used in other areas of machine learning, such as image processing and computer vision.