Architectural Transformations: Design Space Exploration

Related lectures (27)

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Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.

Covers Singular Value Decomposition, focusing on its application in image compression and data representation.

Explores the Cholesky factorization method for symmetric positive definite matrices.

Covers the analytical expression of orthogonal projections and reflections in 2D space.

Explores latent factor analysis for movie genre classification based on male versus female leads.

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Covers the construction of an iterative method for linear systems, emphasizing matrix decomposition and convexity.

Covers unsupervised learning with a focus on Principal Component Analysis and the Singular Value Decomposition.