{R}R Dev Notes

Found total of 57 articles.

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

2.4 Vector and Matrix Storage in Memory

A clear, practical guide to how vectors and matrices are stored in computer memory. Learn row-major vs column-major layout, strides, contiguity, tiling, cache behavior, and why memory layout affects both speed and numerical stability in real systems.

2025-09-11

2.3 Overflow, Underflow, Loss of Significance

A clear and practical guide to overflow, underflow, and loss of significance in floating-point arithmetic. Learn how numerical computations break, why these failures occur, and how they impact AI, optimization, and scientific computing.

2025-09-10

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

Chapter 2 — The Computational Model

An introduction to the computational model behind numerical linear algebra. Explains why mathematical algorithms fail inside real computers, how floating-point arithmetic shapes computation, and why understanding precision, rounding, overflow, and memory layout is essential for AI, ML, and scientific computing.

2025-09-07

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

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

Currently page 2 of 2.

Search Logs

Hello World bot 1195 Deploy Teams bot to Azure 1156 IT assistant bot 1152 Microsoft Bot Framework 1065 Teams bot development 1037 Teams production bot 1016 bot for sprint updates 1012 Teams app zip 996 Zendesk Teams integration 994 Microsoft Teams Task Modules 987 Bot Framework Adaptive Card 983 Bot Framework example 975 Task Modules 971 Teams chatbot 970 C 959 Teams bot tutorial 959 Azure CLI webapp deploy 958 Teams bot packaging 957 Bot Framework proactive messaging 948 Graph API token 947 Bot Framework CLI 941 Adaptive Card Action.Submit 936 Bot Framework prompts 924 Azure App Service bot 916 Microsoft Graph 915 Azure Bot Services 897 Adaptive Cards 886 Azure bot registration 883 ServiceNow bot 871 proactive messages 829

Development & Technical Consulting

Working on a new product or exploring a technical idea? We help teams with system design, architecture reviews, requirements definition, proof-of-concept development, and full implementation. Whether you need a quick technical assessment or end-to-end support, feel free to reach out.