SVD: Singular Value Decomposition

Related lectures (23)

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Introduces Singular Value Decomposition (SVD) in linear algebra, covering matrix factorization and properties with practical examples.

Explores matrix inversion, conditions for invertibility, uniqueness of the inverse, and elementary matrices for inversion.

Delves into singular value decomposition and its applications in linear algebra.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

Explores the fundamentals of Singular Value Decomposition, including orthonormal bases and practical applications.

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

Explores the properties of invertible matrices, including unique solutions and linear independence.

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Explores the unique decomposition of matrices into diagonalizable and nilpotent parts, showcasing their properties and applications.

Explores spectral decomposition of symmetric matrices and Singular Value Decomposition (SVD) for matrix decomposition.