**Numpy **is an array-processing package. It is the fundamental package for scientific computing with Python. If you’re working with matrices, you’re going to love working with NumPy. In this lecture, we talk about the most important functionalities that NumPy has to offer. In a nutshell and from an NumPy perspective, we talk about different types of matrices, matrix operations, matrix arithmetic, matrix decompositions and products, inversions, and systems.

Timestamps of this lecture are:

00:00:00 Intro

00:02:31 Jupyter setup

00:06:23 Numpy setup

00:08:16 Markdown cell

00:10:40 Array

00:11:26 type function

00:13:01 Indexing Array elements

00:14:36 Dimensions of Array

00:15:38 Matrix

00:17:36 Extracting a sub-matrix

00:19:22 Modifying matrix elements

00:22:15 Identity matrix

00:22:50 Zeros matrix

00:24:14 Ones matrix

00:24:48 Constant matrix

00:27:48 Random matrix

00:31:11 Mean

00:33:35 Standard Deviation

00:36:49 dtype function

00:38:31 Matrix Addition

00:41:06 Matrix Subtraction

00:41:45 Matrix Point-wise Multiplication

00:43:00 Matrix Point-wise Division

00:46:08 Matrix Products

00:46:44 np.matmul function

00:50:40 np.dot function

00:51:40 np.inner function

00:52:46 np.tensordot

00:55:52 Matrix Exponentiation

00:57:13 Kronecker Product

00:59:14 Matrix Decompositions

00:59:23 Cholesky Decomposition

01:03:06 QR Decomposition

01:05:05 EigenValue Decomposition (EVD)

01:08:58 SingularValue Decomposition (SVD)

01:10:08 Matrix Norms

01:10:10 L2 Frobenius Norm

01:10:24 Condition Number

01:10:56 Determinant of a matrix

01:11:10 Rank of a matrix

01:11:33 Trace of a matrix

01:13:05 Solving Linear Equations Ax = b

01:13:39 Inverse of a matrix

01:14:10 np.linalg.solve function

01:14:56 Moore-Penrose Pseudo-Inverse

01:15:53 Recap

Thanks for all you do Ahmad !