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hadrienj.github.io | ||
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thenumb.at
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| | | | | [AI summary] This text provides an in-depth exploration of how functions can be treated as vectors, particularly in the context of signal and geometry processing. It discusses the representation of functions as infinite-dimensional vectors, the use of Fourier transforms in various domains (such as 1D, spherical, and mesh-based), and the application of linear algebra to functions for tasks like compression and smoothing. The text also touches on the mathematical foundations of these concepts, including the Laplace operator, eigenfunctions, and orthonormal bases. It concludes with a list of further reading topics and acknowledges the contributions of reviewers. | |
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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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austinmorlan.com
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| | | | | It took me longer than necessary to understand how a rotation transform matrix rotates a vector through three-dimensional space. Not because it's a difficult concept but because it is often poorly explained in textbooks. Even the most explanatory book might derive the matrix for a rotation around one axis (e.g., x) but then present the other two matrices without showing their derivation. I'll explain my own understanding of their derivation in hopes that it will enlighten others that didn't catch on right away. | |
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francisbach.com
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| | | [AI summary] This mathematical post explores the geometry of positive semi-definite matrices using the von Neumann entropy and related Bregman divergences to derive concentration inequalities for random matrices. | ||
