矩阵乘法如何去逆矩阵_矩阵乘法和求逆

矩阵乘法如何去逆矩阵

数据科学与机器学习的线性代数 (LINEAR ALGEBRA FOR DATA SCIENCE AND MACHINE LEARNING)

We are going to treat two of the most used calculations for matrices, multiplications, and inversion, let’s start with multiplication and how to do it in distinct ways.

我们将处理两个最常用的矩阵，乘法和求逆计算，让我们从乘法以及如何以不同的方式开始。

矩阵乘法 (Matrix Multiplication)

To be able to multiplicate matrices, their sizes have to be compatible, the number of rows of the first matrix has to match the number of columns of the second matrix.

为了能够相乘矩阵，它们的大小必须兼容，第一矩阵的行数必须与第二矩阵的列数匹配。

We will run all the examples on the same two 2 by 2 matrixes:

我们将在相同的两个2 x 2矩阵上运行所有示例：

Base matrices to run examples, self-generated.

运行实例的基本矩阵，是自生成的。

点积 (Dot product)

The first way to multiplicate them is by using the dot product, that is, multiplicate every row per every column and the index that matches between the two vectors is the position of the result, let’s calculate C = A B.

将它们相乘的第一种方法是使用点积，即将每列的每一行相乘，并且两个向量之间匹配的索引是结果的位置，让我们计算C = AB 。

Let’s explain how to calculate c11 and c12,

让我们解释一下如何计算c11和c12 ，

Dot product example, self-generated.

点产品示例，自行