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Singular Value Decomposition breaks down massive image or text matrices into smaller, memory-efficient approximations.

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Crucial for stability analysis in engineering and principal component analysis (PCA) in data science. Applications of Linear Algebra

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Introduction to matrix types (symmetric, skew-symmetric, orthogonal, and Hermitian matrices).