Next, I'll give a quick review of linear algebra. If you have never been exposed to vectors and matrices, then everything in this courseware is new knowledge to you, or if you have some knowledge of linear algebra before, but have forgotten it due to a long time, then Just follow along and I'll quickly review the linear algebra you'll need.
With them, you can implement and use more powerful linear regression models. In fact, linear algebra is not only widely used in linear regression, but its matrices and vectors will help us implement more machine learning models in the future and be more computationally efficient. It is precisely because these matrices and vectors provide an efficient way to organize large amounts of data, especially when we are dealing with huge training sets. If you are not familiar with linear algebra, if you think linear algebra seems to be a complex and scary The concept, especially for people who have never been exposed to it before, don't worry, in fact, in order to implement the machine learning algorithm, we only need some very, very basic knowledge of linear algebra. Next, you can quickly learn everything you need to know about linear algebra. Specifically, to help you decide whether you need to study the next set of videos, I will discuss what matrices and vectors are, talk about how to add, subtract, and multiply matrices and vectors, and discuss the concepts of inverse and transposed matrices.
1. Matrix and vector
As shown in the figure: this is a 4×2 matrix, that is, 4 rows and 2 columns. If it is a row and a column, then the
dimension of the 4×2 matrix is the number of rows × the number of columns.
Matrix elements (matrix items):
