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The Gradient: Perspectives on AI cover image

Gil Strang: Linear Algebra and Deep Learning

The Gradient: Perspectives on AI

NOTE

The Importance of Orthogonality in Numerical Applications

In linear algebra, orthogonality is crucial for accuracy and understanding. While eigenvectors are important for symmetric matrices, singular vectors are essential for all matrices, especially in data analysis. The singular value decomposition allows us to find orthogonal vectors that produce orthogonal output vectors when multiplied by the matrix. Teaching linear algebra needs to be more efficient to cover singular values and their applications, like PCA. The effort is to move quickly through foundational concepts to reach singular values. It's funny how singular values were often rushed in my own linear algebra education.

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