Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Singular Value Decomposition (

The video tutorial explains symmetric matrices, their properties, and the concept of positive semidefinite matrices. It covers the definition of symmetric matrices, their eigenvalues, and eigenvectors, emphasizing orthogonality. The tutorial also discusses positive semidefinite matrices, their properties, and how they can be decomposed. Finally, it introduces eigen decomposition and its significance in linear algebra, setting the stage for singular value decomposition in the next video.

Describe the Gram-Schmidt process and its relevance to orthogonalization.

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How can a matrix be decomposed into a matrix times its transpose?

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What is the significance of eigen decomposition in linear algebra?

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