How to apply multidimensional vector computing to machine learning?

Multidimensional vector computing has many applications in machine learning. For example, it can be used as an abstract tool to describe data structure and algorithm, and represent data as a set of vectors, through which various operations of data can be calculated and manipulated. In linear regression, feature vectors are matrix representations of input variables, where each row represents a sample and each column represents a feature. Vector space plays an increasingly important role in deep learning. Vector space is an abstract tool to describe data structure and algorithm, which represents data as a set of vectors, and calculates and operates various operations of data through these vectors.