Singular Values and Norms: Understanding Linear Maps with SVD

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Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.

Covers diagonalization of matrices, eigenvectors, linear maps, and least squares method.

Covers bases, transformations, and matrix decompositions in linear algebra.

Introduces orthogonal bases, projection onto subspaces, and the Gram-Schmidt process in linear algebra.

Covers the fundamentals of Singular Value Decomposition, including properties, applications, and error measurement.

Covers Singular Value Decomposition, focusing on its application in image compression and data representation.

Covers normed spaces, dual spaces, Banach spaces, Hilbert spaces, weak and strong convergence, reflexive spaces, and the Hahn-Banach theorem.

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

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