Mathematics, Linear Algebra, Machine Learning, Data Science.
eigenvalues, performing diagonalization, and diving into Singular Value Decomposition (SVD). d = np.linalg.det(A) print(f"Determinant of matrix A: {d:.2f}") Output : Determinant of matrix A: -40.00 If a matrix is invertible (det(A) ≠ 0), it’s also full rank. This means that its rank rk(A) is equal