Context and applications: Simple applications

Related lectures (26)

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Explores spectral and singular value decompositions of matrices.

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

Introduces differential forms on manifolds, covering tangent bundles and intersection pairings.

Covers the Singular Value Decomposition (SVD) of a matrix and its applications.

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.

Covers retractions, tangent bundles, and embedded submanifolds on manifolds with proofs and examples.

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

Covers the concept of singular values in linear algebra and their properties, including diagonalization and practical examples.

Covers optimization on manifolds, focusing on smooth manifolds and functions, and the process of gradient descent.

Covers the Singular Value Decomposition theorem and its applications in practice.