Tag
TimeLord is a Python tool that constructs seeds for CPython's PRNG to produce specified sequences, demonstrating how improbable outcomes can be engineered using deterministic generators.
MIT professor Gilbert Strang releases a new textbook 'Linear Algebra and Learning from Data' that bridges linear algebra with AI, receiving high ratings and adoption at top institutions.
This tweet explains how core AI operations like attention and convolution are based on foundational mathematics such as linear algebra and probability, emphasizing the value of understanding these operations over model names.
The article explains how to reverse-engineer Factorio's pseudo-random number generator to predict in-game random events, using linear algebra and knowledge of the PRNG algorithm.
This paper presents an LLM-driven approach using LLaMA 3 and a performance database to generate dynamic algorithm dispatch heuristics in linear algebra, with a case study on LU factorization to optimize computational performance.
The fourth edition of Sheldon Axler's 'Linear Algebra Done Right' textbook is now available for free as Open Access in multiple languages, featuring new exercises and improvements, with print versions also available.
Kirk Borne shares a recommendation for a textbook on linear algebra for data science, machine learning, and signal processing, with links to Amazon and praise from academics.
This paper improves theoretical bounds on the depth of ReLU networks needed to represent the maximum function, showing exact two-hidden-layer representations for up to 10 inputs and improved depth for larger n via exact linear algebra techniques.
A free PDF of the Linear Algebra textbook by David Cherney, Tom Denton, and Andrew Walton from UC Davis is shared, emphasizing its importance for AI and data science.
An immersive linear algebra textbook with interactive figures, covering vectors, matrices, determinants, eigenvalues, and more.
A historical account of the development of the singular value decomposition (SVD) up to 1993, tracing its origins and key contributions. The paper is a classic reference for the mathematical underpinnings of many modern data analysis and machine learning techniques.
This article examines the equivalence between the component and geometric definitions of the vector dot product in Euclidean space, providing a geometric proof using the law of cosines and a projection proof using orthonormal basis vectors.
This blog post explains how to derive the Singular Value Decomposition (SVD) from scratch by focusing on the underlying intuition and the motivation behind the concept, arguing that traditional math books often present formalized conclusions without showing the exploratory path.
Gilbert Strang presents an updated vision for teaching linear algebra through a YouTube playlist and MIT OpenCourseWare site.
A Twitter thread shares a curated list of 10 free, legally downloadable textbooks from MIT, Stanford, Berkeley, and Harvard, covering topics like linear algebra, machine learning, probability, and data science.
A tweet announces that University of Michigan's ROB 501, a 26-lecture series covering the mathematical foundations of robotics, is available for free on YouTube with open-source materials on GitHub.
University of Michigan Robotics shares free open-source course materials including lectures, textbooks, and projects from their top robotics program, covering topics from computational linear algebra to autonomous systems.
The University of Michigan has made its entire robotics degree curriculum freely available on GitHub, including lecture videos, textbooks, and assignments, starting with practical linear algebra for robotics.
A tweet showcasing a CuTe DSL kernel sample that uses layouts to express transposition, part of the FlashAttention-4 kernel.
A curated GitHub collection (Mathematics for Machine Learning) that organizes books, papers, video lectures, and math basics for learning the math behind machine learning, covering linear algebra, calculus, probability, statistics, and more.