Matrix muliplication operator `@` in python

In Python, the @ operator is used to perform matrix multiplication between two arrays when using the NumPy library. Matrix multiplication is an important operation in linear algebra, and it has many applications in machine learning, deep learning, data science, and other fields.

To perform matrix multiplication in NumPy, we can use the @ operator between two NumPy arrays. For example, suppose we have two NumPy arrays a and b:

import numpy as np

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])

To perform matrix multiplication between a and b, we can use the @ operator:

c = a @ b

This will result in a new NumPy array c, which is the result of multiplying a and b using matrix multiplication.

Before Python 3.5, matrix multiplication was performed using the np.dot() function instead of the @ operator. However, the @ operator was introduced in Python 3.5 to make matrix multiplication more readable and intuitive.

In summary, the @ operator in Python is a useful tool for performing matrix multiplication with NumPy arrays. Its introduction in Python 3.5 has made it easier to work with matrices in Python and has simplified the code needed to perform this important mathematical operation.