{R}R Dev Notes

Found total of 61 articles.

4.0 Solving Ax = b

A deep, accessible introduction to solving linear systems in numerical computing. Learn why Ax = b sits at the center of AI, ML, optimization, and simulation, and explore Gaussian elimination, pivoting, row operations, and failure modes through intuitive explanations.
2025-09-17

3.2 Measuring Errors

A clear and intuitive guide to absolute error, relative error, backward error, and how numerical errors propagate in real systems. Essential for understanding stability, trustworthiness, and reliability in scientific computing, AI, and machine learning.
2025-09-14

3.1 Norms and Why They Matter

A deep yet accessible exploration of vector and matrix norms, why they matter in numerical computation, and how they influence stability, conditioning, error growth, and algorithm design. Essential reading for AI, ML, and scientific computing engineers.
2025-09-13

Chapter 3 — Computation & Mathematical Systems

A clear, insightful introduction to numerical computation—covering norms, error measurement, conditioning vs stability, and the gap between mathematical algorithms and real implementations. Essential reading for anyone building AI, optimization, or scientific computing systems.
2025-09-12

2.2 Machine Epsilon, Rounding, ULPs

A comprehensive, intuitive guide to machine epsilon, rounding behavior, and ULPs in floating-point arithmetic. Learn how precision limits shape numerical accuracy, how rounding errors arise, and why these concepts matter for AI, ML, and scientific computing.
2025-09-09

2.1 Floating-Point Numbers (IEEE 754)

A detailed, intuitive guide to floating-point numbers and the IEEE 754 standard. Learn how computers represent real numbers, why precision is limited, and how rounding, overflow, subnormals, and special values affect numerical algorithms in AI, ML, and scientific computing.
2025-09-08

1.4 A Brief Tour of Real-World Failures

A clear, accessible tour of real-world numerical failures in AI, ML, optimization, and simulation—showing how mathematically correct algorithms break inside real computers, and preparing the reader for Chapter 2 on floating-point reality.
2025-09-06

1.3 Computation & Mathematical Systems

A clear explanation of how mathematical systems behave differently inside real computers. Learn why stability, conditioning, precision limits, and computational constraints matter for AI, ML, and numerical software.
2025-09-05

Numerical Linear Algebra: Understanding Matrices and Vectors Through Computation

Learn how linear algebra actually works inside real computers. A practical guide to LU, QR, SVD, stability, conditioning, and the numerical foundations behind modern AI and machine learning.
2025-09-01

Message Extensions｜Mastering Microsoft Teams Bots 4.3

Learn how to build search- and action-based Message Extensions in Microsoft Teams. This section shows how to let users interact with your bot directly from the message composer — to search records, fill forms, or insert rich cards — all without leaving the chat.
2025-04-13

Overview of Microsoft Teams Bot Capabilities｜Mastering Microsoft Teams Bots 1.3

Explore the full range of capabilities bots can offer in Microsoft Teams. This section breaks down interactive contexts, features like Adaptive Cards, proactive messaging, user authentication, Graph API integration, and what limitations still exist. Get a developer’s guide to what’s possible.
2025-04-04

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