Books

Gradient-boosted trees have dominated tabular ML for a decade. A new class of pretrained models just broke through — making accurate predictions on unseen datasets in seconds, with zero gradient steps on your data. This book explains why it works, when it fails, and how to deploy it.

If a humanoid robot carries about $28,000 a year in fixed cost, then at $60 contribution per billable hour it breaks even at roughly 467 billable hours per year, or just under 9 hours per week. But if supervision is heavier and contribution drops to $28 per hour, break-even jumps to 1,000 hours per year, or about 19.2 hours per week.

Learn Next.js by building a real AI-powered application, from project setup to production deployment. This book covers the complete journey: TypeScript, React, Next.js App Router, Prisma, authentication, the Vercel AI SDK (chat, RAG, tool calling), testing, security, and deployment. One project, 28 chapters, no toy demos.

Artificial Intelligence has become the new global power race. Nations are competing for data, infrastructure, and algorithmic dominance that will define the future world order.

The game theory already inside your ML toolkit, explained for working engineers. Covers GANs, SHAP, auctions, strategic classification, MARL, federated learning, and LLM alignment.

AI made creation cheaper. It also made judgment more valuable.Taste: Turning Vibe into Assets in the AI Age shows why the next wave of winners will not simply be the people with the best credentials or the biggest teams, but the ones who can turn instinct, clarity, and initiative into real assets.

Master machine learning algorithms through worked examples and hands-on practice problems. From logistic regression to neural networks, this companion exercise book provides step-by-step solutions and progressive difficulty levels to build deep understanding.

It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.

Master the art of AI interaction with 22 proven prompting techniques, real code examples, and production-tested strategies. From the creator of GitHub's most-starred prompt engineering repository (7,100+ stars).

Stop guessing which machine learning algorithm to use. This book provides clear mathematical explanations, decision frameworks, and real-world examples to help you select, implement, and evaluate models correctly from data to deployment.

Digital Transformation in the Era of AI reframes transformation as a continuous capability, not a one-time program. Drawing on real enterprise experience, this book shows how organisations can evolve their architectures, operating models, and leadership practices to harness AI responsibly and at scale, without breaking what already works.

Most people think they are bad at math. In reality, they were never taught arithmetic properly.This book is a modern English edition of Arithmetic by Alexander P. Kiselev—the text that formed the backbone of mathematical education in Russia and USSR for over a century and helped produce generations of exceptionally strong mathematicians, scientists, and engineers.Unlike modern textbooks that prioritise shortcuts, visuals, and lowered expectations, Kiselev builds arithmetic logically, systematically, and rigorously. Every method is explained. Every operation has meaning. Exercises are carefully sequenced to develop real understanding—not rote pattern-following.This book does not promise “easy math”. It promises something better: clarity, confidence, and competence.Whether you are a student, a parent, a tutor, or an adult rebuilding fundamentals, this book will change how you understand arithmetic—and why so much later mathematics suddenly becomes easier.

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

Longitudinal data are powerful but complex, requiring new concepts, data structures, and models that can feel overwhelming to learn. This cheat sheet brings together the key ideas, R commands, and modelling approaches into a single workflow, helping you understand how everything fits together and providing the building blocks for mastering longitudinal data analysis.

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.