How can I use NumPy for advanced linear algebra?

How can I use NumPy for advanced linear algebra?

You can use NumPy for various advanced linear algebra operations like matrix decompositions, eigenvalue problems, and solving linear systems. What specific operation are you interested in?

Can you explain how to perform singular value decomposition?

Sure! You can perform Singular Value Decomposition (SVD) in NumPy using the numpy.linalg.svd() function. It decomposes a matrix into three other matrices. Would you like a code example?

Yes, please provide a code example.

Here's a simple example:

import numpy as np

A = np.array([[1, 2], [3, 4]])
U, S, VT = np.linalg.svd(A)
print('U:', U)
print('Singular values:', S)
print('VT:', VT)

This will give you the matrices U, S, and VT from the SVD of matrix A.

What are some applications of SVD in data science?

SVD is widely used in data science for dimensionality reduction, image compression, and recommendation systems. It helps in extracting latent features from data.