Books

Bridge the gap between HYSYS simulations and industrial reality using Physics-Informed AI and MATLAB

Machine learning doesn’t fail in theory—it fails in production. This book shows you how to build PyTorch systems that remain robust when data shifts, assumptions break, and reliability matters.

A hands-on guide for PHP developers who want to use AI and machine learning in real projects. No hype, no math—just practical ideas, tools, and PHP code that works.

What if AI didn’t just answer questions, but actually did the work? This book takes you from basic AI concepts to autonomous agents that can reason, plan, and act. No hype. No heavy math. Just clear explanations, real examples, and a builder’s mindset.

Are you interested in starting or returning to Machine Learning? This book offers a concise, straightforward view into the field and fundamental techniques that see use even in high-level career roles. Through a combination of examples, math and coding projects, the book will grow the reader's confidence in being a Machine Learning practitioner.

Building generative AI application is not only about LLM choice and prompt engineering, but also about the well-architected cloud solution.

Mastering Advanced Time Series Forecasting in Python is the definitive sequel to the #1 forecasting bestseller. Designed for practitioners who want to go beyond ARIMA and basic ML, this book takes you deep into probabilistic forecasting, hierarchical coherence, and cutting-edge foundation models—backed by production-ready Python code. Learn how to assess forecastability, build scalable pipelines, quantify uncertainty, and deploy systems that deliver real business impact. Written by a globally recognized expert whose methods power multimillion-dollar decisions, this is the practical, honest, and advanced guide every data scientist, ML engineer, and quantitative professional needs to master modern forecasting.

Build a real AI platform on OpenShift, not just “another Kubernetes cluster.” This guide walks you through air-gapped installs, Quay mirroring, GPUs, InfiniBand, GitOps, and benchmarking—so platform and SRE teams can deliver a secure, observable, high-performance OpenShift AI environment that app teams actually want to use.

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.

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.

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.

Mastering Forecasting Metrics & Accuracy: For Data Science and BeyondForecasting models are only as good as the metrics used to measure them. Yet many teams still rely on outdated or misleading measures like MAPE. This book is the first comprehensive, practitioner-friendly guide dedicated entirely to forecast evaluation metrics — blending clear theory, Python recipes, and real-world case studies.Learn how to avoid common pitfalls, measure bias, handle intermittent demand, and apply advanced metrics like MASE, RMSSE, CRPS, pinball loss, and calibration scores. Each chapter includes formulas, code, and visuals to make concepts easy to apply.Perfect for data scientists, ML engineers, analysts, researchers, and industry professionals in retail, finance, and energy. No heavy math required.Living book: buy once, get free lifetime updates.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!