Tag: #linear-algebra
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 10 posts
The Math You Need for Robotics, in Order: And What You Can Safely Put Off
An answer to how far you actually need to take your math to build a robot arm. Organized into six branches in order: linear algebra, trigonometry and rotation representations, calculus and multivariable methods, differen
2026-08-02 · 25 min read #robotics#math#electronics#linear-algebra#controlModern C++ 2026 — C++ 23 / 26 / Modules / Reflection / Senders / std::print / CMake 3.30 / Conan 2 / vcpkg Deep Dive
A map of C++ in 2026. C++ 23 (published October 2024 as ISO/IEC 14882:2024) with std::expected, std::print, and std::generator; the real headliners of C++ 26 — Reflection (P2996), Senders/Receivers (P2300), Linear Algebr
2026-05-16 · 27 min read #cpp#cpp-23#cpp-26#modules#rangesEngineering Math Series 14: Diagonalization and Dynamic Systems
Explains why diagonalization simplifies computation and how it connects to time-response analysis of systems of differential equations, with examples.
2026-03-19 · 3 min read #engineering-math#linear-algebra#diagonalization#dynamic-systems#2026-03Engineering Math Series 11: Matrices and Linear Systems
Understand matrices not as mere arrays of numbers but as a language for compactly expressing linear systems, and connect them to systems of linear equations.
2026-03-19 · 3 min read #engineering-math#linear-algebra#matrices#linear-systems#2026-03Engineering Math Series 13: Eigenvalues and Eigenvectors
Explains the concepts of eigenvalues and eigenvectors -- how much a matrix stretches or shrinks specific directions -- with intuition and worked examples.
2026-03-19 · 3 min read #engineering-math#linear-algebra#eigenvalues#eigenvectors#2026-03Engineering Math Series 12: Elimination, Inverse Matrices, and Determinants
Organizes the three core perspectives for solving linear systems -- Gaussian elimination, inverse matrices, and determinants -- at the beginner level.
2026-03-19 · 3 min read #engineering-math#linear-algebra#determinants#matrix-inverse#2026-03Mathematical Foundations for AI/ML: Complete Guide - Linear Algebra, Calculus, Probability
A complete guide to mastering the core mathematics required to understand AI and machine learning. Covers linear algebra (vectors, matrices, eigenvalues), calculus (partial derivatives, chain rule), probability and stati
2026-03-17 · 21 min read #mathematics#linear-algebra#calculus#probability#statisticsLinear Algebra Complete Guide: Zero to Hero from Vectors to SVD
A comprehensive guide to linear algebra from first principles to advanced topics. Covers vectors, matrices, determinants, vector spaces, eigenvalues, and SVD with Python code examples throughout.
2026-03-17 · 23 min read #linear-algebra#math#engineering-math#vectors#matricesMath + LaTeX/KaTeX Complete Guide for Reading AI/ML Papers
A comprehensive summary of essential math concepts (linear algebra, calculus, probability/statistics, optimization) and LaTeX/KaTeX formula syntax for reading AI/ML papers with practical examples, symbol cheat sheets, an
2026-03-08 · 20 min read #ai-papers#math#latex#katex#linear-algebraComplete Math Guide for AI — From Linear Algebra to Information Theory
A guide to the math needed for AI/deep learning, explained with code and intuition. Linear algebra (matrices, eigenvalues), calculus (partial derivatives, backpropagation), probability/statistics (Bayes, distributions),
2026-03-02 · 13 min read #ai#mathematics#linear-algebra#calculus#probability