Machine Learning

This reference volume consists of revised, edited, cross-referenced, and thematically organized articles from the Software Diagnostics and Observability Institute and the Software Diagnostics Library (former Crash Dump Analysis blog) about software diagnostics, root cause analysis, debugging, crash and hang dump analysis, and software trace and log analysis written from 15 April 2024 to 14 November 2025.

PLEASE NOTE THAT THIS IS NOT THE FULL BOOK BUT JUST CHAPTER 1 and 2 because a simple topic like Simple Linear Regression became 20+ pages long. As a full-time frontend architect, I'm writing and releasing this book chapter by chapter, at a pace that fits around my day job and life. So, think of this as a journey we're taking together, one concept at a time. REST OF THE CHAPTERS WILL BE AVAILABLE AT REGULAR INTERVALS. So keep me motivated by subscribing :)

Most AI systems fail in production. They chain prompts together and call it "agentic". They collapse under real-world pressure. The gap isn't the technology – it's missing infrastructure. This book bridges that gap. Learn the architecture patterns and infrastructure to build autonomous AI systems that work at scale. Master what distinguishes real agents from chatbots with tools. Deploy with confidence. Stop building demos. Start shipping production agentic AI.

Discover how modern web scraping goes beyond simple scripts. Learn to extract, automate, and transform data from dynamic websites using Python, Playwright, undetected-chromedriver, Chrome DevTools MCP, and AI tools. Packed with hands-on examples and expert tips, this book shows you how to build ethical, scalable, and intelligent scraping workflows that turn raw web content into actionable insights.

This book presents a modern approach to system identification and adaptive control through the lens of online machine learning. It bridges theory and practice, guiding readers from classical linear control to advanced nonlinear adaptive methods with MATLAB examples. Designed for students, researchers, and engineers, it provides the knowledge and tools to design intelligent control systems for real-world applications.

Bridge AI and science with this hands-on guide. Whether you're a researcher learning ML or an engineer entering scientific applications, build real systems across chemistry, biology, physics & climate. Master Transformers, Diffusion Models & GNNs for scientific discovery. 500+ pages, 50+ Colab notebooks. Design molecules, predict proteins, accelerate climate models—all hands-on, zero setup required.

A model that wins on MAPE can lose money on the shelf. A 15% accuracy gain can hide a 12% bias. An interval that looks tight can be wrong a third of the time.Forecast metrics are decision instruments, not neutral truth detectors — and most teams are still choosing them by habit. This book shows where MAE, MAPE, sMAPE, WAPE, MASE, RMSSE, CRPS, pinball loss, energy scores, bias measures and cost-based metrics help, and where they quietly mislead.Twelve chapters take you from elicitation and baselines through intermittent demand, probabilistic and hierarchical scoring, and the accuracy–utility gap, to a production evaluation system with monitoring, drift detection, alerting and governance. Every metric arrives with its assumptions, every criticism carries a remedy, and a retrospective Walmart M5 case study runs throughout, with Python listings and reproducibility checks.For demand planners, forecasting data scientists, ML engineers and analytics leads who need evaluation choices they can defend. Measure what matters.

So how do you make an AI agent and conversational agent understand time? How does time shape attention? How is time important for the context engine? You will learn how to add time to knowledge graphs, how time and causality drive context, and how to make the knowledge graphs that are used for AI memory time-aware.

Helping C# and .NET developers to learn how to do machine learning and become highly sought-after (and well-paid) AI engineers. No prior experience of ML required!

หนังสือเล่มนี้เป็นคู่มือฉบับกระชับพร้อมภาพประกอบ สำหรับผู้ที่อยากเข้าใจการทำงานภายในของแบบจำลองภาษาขนาดใหญ่ ในบริบทการสัมภาษณ์ ทำโครงการ หรือเพื่อสนองตอบความใคร่รู้ของตนเอง

Demystify the mathematical foundations of machine learning with this concise, hands-on guide. Dive into the essentials of linear algebra, calculus, and statistics, specifically tailored for practical application with Python and NumPy. No more wading through dense textbooks – get the core concepts you need to build intelligent systems, fast.

You’ll get a clear, beginner-friendly introduction to AI and machine learning, explanations of neural networks, data, and training processes, practical insights into how AI really works (without hype) and a hands-on PyTorch project to build your own small AI

Turn uncertainty into a competitive advantage with probabilistic forecasting and Conformal Prediction.

A powerful new book on Conformal Prediction by bestselling author and machine learning expert Valery Manokhin, bridging theory and real-world machine learning. Discover how to quantify uncertainty with statistical guarantees—across deep learning, time series, forecasting, and more. Preorder now before the price goes up.

I wanted to understand how ChatGPT and other large language models (LLMs) really work, so I read a lot of books, watched YouTube videos, asked hundreds of questions, and wrote it all down. This book is the result. If you want to understand how large language models like ChatGPT actually work, from tokens and vectors to transformers and training, this book will explain it in a clear, approachable way.