Singular Value Decomposition: Fundamentals

Related lectures (27)

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Covers the Singular Value Decomposition (SVD) in detail, including properties of matrices and system linearity.

Explains the method of least squares in linear regression to find the best-fitting line to a set of data points.

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

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

Covers the theory and applications of Singular Value Decomposition in computational physics, including solving linear systems and polynomial fits.

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

Explores spectral and singular value decompositions of matrices.

Explains the step-by-step process of finding the singular value decomposition of a matrix.

Introduces the Gram-Schmidt algorithm, QR factorization, and the method of least squares.