In this tutorial, we will learn how to use Numpy’s allclose() function to compare two Numpy arrays. Numpy’s allclose() function compares two arrays element-wise for similarity and tell us if all the elements close enough as defined tolerance specified by user. It returns True if all the elements are close between two arrays and False… Continue reading Numpy allclose() function with examples
How to do matrix multiplication with Numpy
In this post, we will learn how to perform matrix multiplication with Pythin in Numpy. In Pythn we have at least a few ways to do matrix multiplication. Here we will learn how to use the Numpy function matmul(), which short for Matrix Multiplication. To use Numpy’s matmul() function, we simply pass in the two… Continue reading How to do matrix multiplication with Numpy
SVD with Numpy
In this post we will learn how to perform Singular Value Decomposition (SVD) in Python with Numpy. We will start with learning the basics of SVD and then learn to use Numpy’s linalg.svd() function to perform SVD in Python with multiple examples. It has a wide range of applications, including data compression, image processing, and… Continue reading SVD with Numpy
Numpy reshape function with examples
NumPy’s reshape() function allows you to change the shape of an array. It takes two required arguments: the array you want to reshape and the new shape you want to give it. Here is the basic syntax of Numpy’s reshape() function. It takes three arguments, the first is the array we want to reshape and… Continue reading Numpy reshape function with examples
How to do QR Decomposition in Python with Numpy
QR decomposition, also known as QR factorization, is a method for decomposing a matrix into the product of an orthogonal matrix and an upper-triangular matrix. It is a useful tool for solving systems of linear equations, computing the inverse of a matrix, and computing the singular value decomposition (SVD) of a matrix. In this blog… Continue reading How to do QR Decomposition in Python with Numpy