Ah, yes, the thrill of a 2501-page tome on matrix calculus—because who doesn't love diving into equations without a floatation device? 🤯 Sponsored by the "Let's Make Math Unnecessarily Intimidating Foundation," it's perfect for anyone whose New Year's resolution was to pretend they understand machine learning jargon. 🤓✨
https://arxiv.org/abs/2501.14787 #matrixcalculus #mathhumor #machinelearning #readingchallenge #mathintimidation #2023resolutions #HackerNews #ngated
Matrix Calculus (for Machine Learning and Beyond)
This course, intended for undergraduates familiar with elementary calculus and linear algebra, introduces the extension of differential calculus to functions on more general vector spaces, such as functions that take as input a matrix and return a matrix inverse or factorization, derivatives of ODE solutions, and even stochastic derivatives of random functions. It emphasizes practical computational applications, such as large-scale optimization and machine learning, where derivatives must be re-imagined in order to be propagated through complicated calculations. The class also discusses efficiency concerns leading to "adjoint" or "reverse-mode" differentiation (a.k.a. "backpropagation"), and gives a gentle introduction to modern automatic differentiation (AD) techniques.
Matrix Calculus (for Machine Learning and Beyond)
This course, intended for undergraduates familiar with elementary calculus and linear algebra, introduces the extension of differential calculus to functions on more general vector spaces, such as functions that take as input a matrix and return a matrix inverse or factorization, derivatives of ODE solutions, and even stochastic derivatives of random functions. It emphasizes practical computational applications, such as large-scale optimization and machine learning, where derivatives must be re-imagined in order to be propagated through complicated calculations. The class also discusses efficiency concerns leading to "adjoint" or "reverse-mode" differentiation (a.k.a. "backpropagation"), and gives a gentle introduction to modern automatic differentiation (AD) techniques.
📚🔢 Ah, yet another thrilling bedtime story about matrix calculus—because who doesn't want to dive into a sea of math symbols with more enthusiasm than a toddler in a ball pit? 🙄 With authors who remind us that they have credentials longer than the paper itself, this is basically "Matrix Calculus for Dummies" but with extra academic snooze. 💤
https://explained.ai/matrix-calculus/ #matrixcalculus #bedtimefun #mathhumor #academicwriting #dummiesguide #HackerNews #ngated
The Matrix Calculus You Need For Deep Learning
Most of us last saw calculus in school, but derivatives are a critical part of machine learning, particularly deep neural networks, which are trained by optimizing a loss function. This article is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed.
The Matrix Calculus You Need For Deep Learning
Most of us last saw calculus in school, but derivatives are a critical part of machine learning, particularly deep neural networks, which are trained by optimizing a loss function. This article is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed.
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