Скачать или смотреть Linear Algebra and Matrix Operations Using Python

Matrix multiplication
B = np.array([[1, 2], [3, 4]])
C = np.dot(A, B)

Transpose of a matrix
A_transpose = np.transpose(A)

Trace of a matrix
trace = np.trace(A)

Determinant of a matrix
det = np.linalg.det(A)

Inverse of a matrix
A_inv = np.linalg.inv(A)

Overall, Python provides a wide range of tools and libraries for performing linear algebra operations, which makes it an excellent choice for data analysis, machine learning, and other scientific computing applications.

Session 3 by Parag Dhawan STTP on Data Science with Statistical Methods Using Python DSSMP 2022

Linear Algebra
-NumPy Arrays
-Array Attributes
Matrix Operations and Functions
-Arithmetic Operations
-Matrix Multiplication
-Matrix Powers
-Matrix Tranpose
-Matrix Inverse
-Matrix Trace
-Matrix Determinant
Examples
-Characteristic Polynomials and Cayley-Hamilton Theorem
-Projections of vectors
Solving Linear Systems
Eigenvalues and Eigenvectors

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