Artificial Intelligence

Accountability cannot be delegated. Execution can. AI can act at a speed and scale that no person can review one decision at a time. The answer is not ceremonial human oversight. It is to establish who owns the workflow, what authority the system has, which limits are enforced and who can intervene when something goes wrong.Human Accountable for the Loop is a practical framework for doing that.

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.

هذا الكتاب هو دليل مختصر وموضّح بالرسوم لأي شخص يرغب في فهم الآلية الداخلية للنماذج اللغوية الضخمة، سواء في سياق المقابلات أو المشاريع أو بدافع الفضول الشخصي.

Have you ever been curious about how your phone unlocks when it sees your face, how a camera can track people and objects in a video, how humans see depth, or how computers can differentiate dogs from cats? This book will start from the basics of image manipulation and build up to cover all of these topics, and more!

AI governance is moving from principle to practice. This hands-on guide shows you how to build, implement and certify an ISO/IEC 42001 AI management system with confidence. From clause-by-clause guidance to practical templates, integration strategies and real-world scenarios, it turns a complex standard into a clear path from foundation to certification.

Build GPT-2, Llama 3, and DeepSeek from scratch in PyTorch. Every chapter has runnable end-to-end code and loads real pretrained weights. Goes well past where most LLM tutorials stop.

Master AI-powered infrastructure automation with this hands-on guide to building production-ready MCP servers and AI agents in Go. Transform from manual AWS operations to intelligent automation that understands your environment and makes smart decisions while keeping humans in control.

Este é um guia dinâmico, prático e visual para o novo e surpreendente mundo da IA Generativa. É como uma versão estendida do vídeo viral do Henrik com o mesmo nome. Versão impressa: Capa comum e Capa dura estão disponíveis na Amazon. Use o site da Amazon do seu país (ex: Amazon.se para a Suécia) para minimizar o tempo e custo de envio.

STOP building fragile AI wrappers. START designing resilient AI systems. Lots of companies are trying to make their small AI experiments into big products, but they don't have a good plan. Engineers need a practical guide to build these new AI systems the right way - so they can handle scale, be reliable, and won't cost too much. This book is that guide. It explains how to design systems that use AI models. This book breaks down the architecture of real AI applications, like an AI-powered code editor or a smart learning app. It gives you a deep, practical look at the real-world challenges and solutions for building these systems. It discusses system design concepts for systems that use LLMs.

Build production-grade RAG systems in C# — from an 80-line Hello World to a fully deployed Azure pipeline with the Microsoft Agent Framework, MCP, GraphRAG, multi-agent orchestration, eval gates, and EU AI Act-ready audit trails. 668 pages, 25 chapters, one evolving enterprise project, every line of code runnable in .NET 10.

AI doesn’t fail loudly. It generates code that looks correct and compiles anyway. This book shows you how to make AI dependable by building the context and guardrails that keep your team shipping instead of debugging.

The is more to AI than Large Language models. Here we explore Symbolic AI with the Prolog language.

An agent can score well on average and still fail exactly where it matters. Beyond “Ship and Pray” shows how to replace benchmark averages with designed experiments, geometric ground truth and failure attribution—so teams can discover when an agent breaks, identify the responsible component and test whether it fails safely under tool faults.

Build real AI products with TypeScript. Learn LLMs, RAG, Agents, MCP, and production AI engineering from a frontend developer's perspective.

Documentation format explains 10 to 127 times more variance in AI-generated code than model choice. We ran 21,462 tests to prove it. This book shows you which formats work, which ones break, and why the industry standard is the worst option for AI consumption.