{R}R 開発ノート

合計 122 件の記事が見つかりました。

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

3.4 Exact Algorithms vs Implemented Algorithms

Learn why textbook algorithms differ from the versions that actually run on computers. This chapter explains rounding, floating-point errors, instability, algorithmic reformulation, and why mathematically equivalent methods behave differently in AI, ML, and scientific computing.
2025-09-16

3.3 Conditioning of Problems vs Stability of Algorithms

Learn the critical difference between problem conditioning and algorithmic stability in numerical computing. Understand why some systems fail even with correct code, and how sensitivity, condition numbers, and numerical stability determine the reliability of AI, ML, and scientific algorithms.
2025-09-15

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

2.4 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

2.2 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

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

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

1.2 Floating-Point Reality vs. Textbook Math

Floating-point numbers don’t behave like real numbers. This article explains how rounding, cancellation, and machine precision break AI systems—and why it matters.
2025-09-04

1.1 What Breaks Real AI Systems

Many AI failures come from numerical instability, not algorithms. This guide explains what actually breaks AI systems and why numerical linear algebra matters.
2025-09-03

1.0 Why Numerical Linear Algebra Matters

A deep, practical introduction to why numerical linear algebra matters in real AI, ML, and optimization systems. Learn how stability, conditioning, and floating-point behavior impact models.
2025-09-02

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

Use Case: Helpdesk Assistant Bot｜Mastering Microsoft Teams Bots 6.1

Explore how to build a Helpdesk Assistant Bot in Microsoft Teams. Learn how bots can reduce IT load by handling FAQs, logging support tickets, and notifying users — all within Teams. This section explains features, user experience, and implementation strategies.
2025-04-18

Proactive Messaging｜Mastering Microsoft Teams Bots 4.2

Learn how to build bots that initiate conversations in Microsoft Teams. This section explains proactive messaging — including when and how to use it, how to store conversation references, and best practices to ensure your bot helps without interrupting.
2025-04-12

Bot Authentication and Identity｜Mastering Microsoft Teams Bots 2.3

Learn how Microsoft Teams bots authenticate users and access secure data. This section covers SSO, OAuth 2.0, and the Microsoft Graph API, giving your bot the ability to understand identity and act on behalf of users—safely and seamlessly.
2025-04-07

Why Build Bots for Microsoft Teams?｜Mastering Microsoft Teams Bots 1.1

Discover why Microsoft Teams bots are transforming the workplace. This section explores the real-world impact of building bots in Teams, from automating tasks and integrating external services to enabling context-aware digital assistants. Learn how bots can save time, boost productivity, and bring automation into the flow of daily work.
2025-04-02

Mastering Microsoft Teams Bots: A Complete Developer’s Guide

The definitive guide to building bots for Microsoft Teams—from fundamentals to deployment. Learn how to build intelligent and interactive bots using the Microsoft Bot Framework, integrate Adaptive Cards and Task Modules, send proactive messages, authenticate users with Teams SSO, and deploy securely on Azure. Packed with practical examples and real-world use cases, this book will help you automate workflows, enhance collaboration, and deliver smart experiences inside Teams.
2025-04-01

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