The Linear Algebra module of NumPy offers various methods to apply linear algebra on any numpy array.

Learn NumPy Linear Algebra in just ONE VIDEO !!

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

Instructor: Dr. Ahmad Bazzi

NumPy: http://www.numpy.org/

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