If you are not familiar with matrix multiplication. In this article, we will learn different ways of multiplying matrices from an easy-to-read function to an optimized code.

In this article, we will learn different ways of multiplying matrices from an easy-to-read function to an optimized code.

If you had read my previous articles on matrix operations, by now you would have already know what a matrix is. Yes, a matrix is a `2D`

representation of an array with `M`

rows and `N`

columns. The shape of the matrix is generally referred to as dimension. Thus the shape of any typical matrix is represented or assumed to have (`M`

x `N`

) dimensions.

- Row Matrix — Collection of identical elements or objects stored in
`1`

row and`N`

columns. - Column Matrix — Collection of identical elements or objects stored in
`N`

rows and`1`

column.

**Note** — Matrices of shapes (`1`

x `N`

) and (`N`

x `1`

) are generally called row vector and column vector respectively.

Learn numpy features to see why you should use numpy - high performance, multidimensional container, broadcasting functions, working with varied databases

Learn the uses of numpy - Alternate for lists in python, multi dimensional array, mathematical operations. See numpy applications with python libraries.

And how you can use DFS and BFS. If you’ve read an Introduction to Competitive Programming, then you’re probably familiar with why Competitive Programming is important.

Learn NumPy Copy and View - Deep Copy, shallow copy and No copy in NumPy, NumPy view creation and types with examples, NumPy View vs Copy

Python is an open-source object-oriented language. It has many features of which one is the wide range of external packages. There are a lot of packages for installation and use for expanding functionalities. These packages are a repository of functions in python script. NumPy is one such package to ease array computations.