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everyone in ML/AI in any capacity should drop what they're doing and master this book if they really want to build competitive models stat.ucla.edu/~ywu/GTP.pdf

"The USSR Olympiad Problem Book" 320 unconventional problems in algebra, arithmetic, elementary number theory, and trigonometry. Archive link: archive.org/details/shklar…

Tensor Decompositions for Data Science [and Computational Science]: amzn.to/4s2B5g1 —— #ML #MachineLearning #DataScientist #DataScience #Mathematics —— Note: Extensive background materials in linear algebra, optimization, probability, and statistics are included as appendices.

New book in town: "Elements of Deep Learning" link.springer.com/book/10.1007/9…

Book #OTD "The Geometry of Uncertainty: The Geometry of Imprecise Probabilities" link.springer.com/book/10.1007/9…

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Congratulations! Pokemon Center Bangkok 🎉 แฟนอาร์ตต้อนรับข่าวดีค่ะะ โปเกมอนเซ็นเตอร์จะมาเปิดที่ไทยแล้ววว ดีใจมากๆ เลย ในที่สุดก็มีวันนี้

Book #OTD "Topology of Infinite-Dimensional Manifolds"

FREE Math Book. "Introduction to the Modeling and Analysis of Complex Systems," by Sayama. Complex systems involve a large number of components interacting with each other. Topics: chaos, cellular automata, dynamical systems, discrete-time models, continuous-time models, bifurcations, continuous field models, static networks, dynamic networks, and agent-based models. Python sample codes are provided for each modeling example. Other topics: Ecological and Evolutionary Models, Cobweb Plots for One-Dimensional Iterative Maps, etc. Link: milneopentextbooks.org/introduction-t…

High-Dimensional Probability by Professor Roman Vershynin A highly accessible introduction to modern methods of high-dimensional probability, updated with new material and 200 new exercises. 📚 cup.org/4ujao7D

100 Great Problems of Elementary Mathematics "The collection, drawn from arithmetic, algebra, pure and algebraic geometry and astronomy, is extraordinarily interesting and attractive." —Mathematical Gazette Get it here: amzn.to/4bY2kBI

The Art of Problem Solving (AoPS) - 13 volumes: amzn.to/4orahDE Precalculus Calculus Number Theory Prealgebra Algebra Intermediate Algebra Geometry Probability Solutions Manuals

I recommend this wonderful book for both her life and scientific work by Michèle Audin who recently passsed away.

The Mathematical Universe — From Pythagoras to Planck: amzn.to/4jlakQh With no more than basic high school mathematics, explore and understand better the bizarre world of subatomic particles, honey bees and ants, galaxies, black holes, infinity, and more.

The Machine That Learns The Law Behind The Data A very very interesting US Patent US10963540B2 - Physics Informed Learning Machine describes a learning system that does not begin with data alone. It begins with a physical model, usually written as a differential equation (or PDE) dx/dt = f(x,t) A normal Machine Learning model sees scattered data and tries to fit it. A physics-informed learning machine starts with a law. Then it treats the data as evidence that updates what the model believes about the physical system. For this application, I use the patent idea on NASA C-MAPSS Turbofan engine data. The machine watches multivariate telemetry from a degrading engine and infers a hidden health state that is not measured directly. From that posterior belief, it estimates the engine’s remaining useful life. In the main 3D scene, the engine lifetime is turned into a tunnel. The spiral ribbons are real sensor channels evolving over cycle-time. The glowing core is the inferred health state. The surrounding cloud is uncertainty. The orange wall ahead is the predicted failure horizon. So the big picture is: sensor evidence comes in, posterior belief tightens, and the machine moves from uncertainty toward a concrete failure prediction. The inset posteriors make that explicit. The health posterior shows where the model believes the hidden engine condition sits at the current moment, and how sharply it believes it. The RUL posterior shows the same idea for remaining life... early on it is broad, later it shifts left and narrows as the machine becomes more certain about how close failure is. This idea is not limited to engines. The same idea can apply to data centers, CPUs, GPUs, cooling systems, power grids, robotics, batteries, and any machine that produces telemetry while obeying physical constraints. In an age where machine learning runs on massive hardware infrastructure, this kind of model matters: it can turn noisy sensor streams into early warnings before expensive systems fail.

共形场论 (conformal field theory, CFT) 是在共形变换下不变的量子场论，共形场论在凝聚态物理学、统计力学、量子统计力学以及弦论中有重要应用 计算共形几何（computational conformal geometry, CCG）由丘成桐和其学生顾险峰共同创立并发展的理论，旨在将经典微分几何、Riemanm 面理论、代数拓扑、几何偏微分方程的概念、定理和方法推广至计算机科学领域（推广至离散情形，转换成计算机算法，并广泛应用于计算机图形学、计算机视觉、计算机辅助几何设计、数字几何处理、计算机网络、计算力学、机械设计以及医学影像等领域中） 共形循环宇宙学（conformal cyclic cosmology，CCC）是广义相对论框架下的一个宇宙学模型，由理论物理学家罗杰·彭罗斯提出 Conformal、CFT、CCG、CCC、共形映射、共形对称 共形几何等等，不知它们之间都有着怎样的关联 CFT英文版教材两册 共形场论全2卷【法】P. D. Francesco CFT 是过去几十年里理论物理中最活跃且成果丰硕的研究领域之一 这是一部全面系统介绍共形场论的专著，本书原版共18章 分为3个部分。世图引进的英文版将其分成了两卷 此外，《Introduction to Conformal Field Theory : With Applications to String Theory》是一本与弦理论相关的共形场论著作

Symmetry is a fundamental concept in science and art related to groups and group actions. → selected books presenting symmetry under various angles, from popular viewpoints to advanced mathematical theories with applications in mechanics, physics, machine learning and AI,...

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