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Lecture
Singular Value Decomposition
Description
This lecture covers the concept of Singular Value Decomposition (SVD), explaining how a matrix can be decomposed into three matrices, providing a geometric interpretation and discussing low-rank approximation. It also delves into the fundamental subspaces, the Frobenius norm, and the operator norm, emphasizing the importance of singular values in approximating matrices. The lecture concludes with a detailed explanation of the SVD and its applications in various fields.
Official source
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