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www.eigentales.com | ||
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robotchinwag.com
Deriving the gradients for the backward pass for matrix multiplication using tensor calculus |
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0fps.net
Last time, we showed how one can use symmetric tensors to conveniently represent homogeneous polynomials and Taylor series. Today, I am going to talk about how to actually implement a generic homogeneous polynomial/symmetric tensor class in C++. The goal of this implementation (for the moment) is not efficiency, but rather generality and correctness. If there... |
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francisbach.com
[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. |
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www.jeremykun.com
The singular value decomposition (SVD) of a matrix is a fundamental tool in computer science, data analysis, and statistics. It's used for all kinds of applications from regression to prediction, to finding approximate solutions to optimization problems. In this series of two posts we'll motivate, define, compute, and use the singular value decomposition to analyze some data. (Jump to the second post) I want to spend the first post entirely on motivation and background. |
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