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vxy10.github.io | ||
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stephenmalina.com
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| | | | | Matrix Potpourri # As part of reviewing Linear Algebra for my Machine Learning class, I've noticed there's a bunch of matrix terminology that I didn't encounter during my proof-based self-study of LA from Linear Algebra Done Right. This post is mostly intended to consolidate my own understanding and to act as a reference to future me, but if it also helps others in a similar position, that's even better! | |
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peterbloem.nl
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| | | | | [AI summary] The pseudo-inverse is a powerful tool for solving matrix equations, especially when the inverse does not exist. It provides exact solutions when they exist and least squares solutions otherwise. If multiple solutions exist, it selects the one with the smallest norm. The pseudo-inverse can be computed using the singular value decomposition (SVD), which is numerically stable and handles cases where the matrix does not have full column rank. The SVD approach involves computing the SVD of the matrix, inverting the non-zero singular values, and then reconstructing the pseudo-inverse using the modified SVD components. This method is preferred due to its stability and ability to handle noisy data effectively. | |
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leanprover-community.github.io
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| | | | | [AI summary] A technical documentation entry for the Lean mathematical library detailing definitions and operations for semimodules, modules, matrices, linear maps, and bilinear forms | |
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loufranco.com
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| | | [AI summary] A developer suggests modifying the traditional TDD Red-Green-Refactor cycle to Green-Refactor-Red to improve daily workflow efficiency. | ||
