NumPy Linear Algebra – Dr. Ahmad Bazzi

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

One thought on “NumPy Programming

  1. Adham Bazzi says:

    Thanks for all you do Ahmad !

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