Specifically, the matrix has three important uses.
The first purpose is to solve linear equations. For example, a two-dimensional matrix can be understood as a set of points in a plane rectangular coordinate system. By calculating the distance between points, clustering, classification or prediction can be completed. Similar operations can be extended to multi-dimensional situations.
The second application is equation reduction, that is, using the quadratic form of matrix to map linearly inseparable data sets to high dimensions and transform them into linearly separable situations, which is one of the basic principles of support vector machines.
The third application is conversion. Matrix can be reduced by eigenvalues and eigenvectors, which simplifies the operation of high-dimensional data sets like pictures. This principle is used in principal component analysis.
In programming, we can formally understand a matrix as a two-dimensional array. Take python as an example. A matrix is a big list with several lists nested inside. Each internal list is equal in length, and each element is an integer or floating-point value. An internal list is a row vector, that is, an object.
Before trying to learn a new language, understanding the design principle of this language can keep you awake and develop when exploring this new language.