Books

Aprende a combinar SQL, Python, estadística y Machine Learning para priorizar posibles fraudes en retail. Un enfoque práctico, técnico y aplicado, con ejemplos, visualizaciones y códigos de apoyo.

不需要机器人实验室，也能真正上手具身智能。

A practical guide to evaluating small and local language models in the real world. Learn how to benchmark reliably, compare models across hardware and runtimes, optimize performance and avoid misleading results. With practical code and rigorous methods, you can build your own evaluation setup and make confident deployment decisions.

A 1,032-page study system for the AIGP exam: every question explained option by option, then linked to the Concept Card that makes it answerable.

What if you could build software without manually writing every line of code? Artificial Intelligence is transforming the way software is designed, developed, tested, and deployed.But becoming an AI-powered software engineer isn't simply about asking an AI to write code.It's about learning how to think, design, build, test, verify, and improve software with AI as your engineering partner.The AI-Powered Software Engineer takes you on a practical journey from traditional programming to AI-assisted development and modern AI engineering.Learn how to use AI to:Design software. Generate and understand code. Debug complex problems. Build AI-powered applications. Automate workflows. Test and secure software. Deploy real products. Build AI-powered SaaS businesses.You don't need to become the fastest programmer in the room.You need to become the person who can turn an idea into a working, reliable, valuable system.Write less code. Think more. Build faster. Engineer smarter.The future of software engineering isn't humans versus AI.It's humans building with AI.

AI is changing fast, and so are the security risks that come with it. This practical guide shows security and technology leaders how to govern, secure and assure AI systems from design through deployment and beyond. Packed with proven frameworks, controls and real-world guidance, it turns complex AI security requirements into practical action.

A vendor-neutral, mechanism-level field guide to operating and extending agentic coding harnesses (542 manuscript pages).

Unlock the full potential of Claude Fable 5.1 with practical prompting techniques built for real-world work. Learn how to get better results from coding, research and complex workflows, avoid common mistakes and build reliable AI systems with proven strategies and ready-to-use prompts.

Most self-hosted LLM deployments run at 10–30% utilization — full retail price for GPUs that mostly sit idle. Tokens per Dollar is a deliberately short field guide for platform engineers: tune vLLM (2–4x throughput on the same hardware), quantize to halve your GPU count, cache the prompt tokens you keep re-paying for, and autoscale on real token traffic instead of CPU. Working YAML throughout, and a case study that cuts a 70B deployment from $29,200 to $8,600 a month with better latency. Read it in an evening; it pays for itself the first day you apply it.

What if your browser could run AI without sending your data to a server? AI in the Browser shows you how to build fast, private and fully local AI applications using WebGPU, WebAssembly and on-device LLMs. From GPU compute to a complete local chat interface, you’ll build everything yourself with practical code.

AI can produce more work in less time. Making that work reliable takes discipline. Disciplined AI Development explores how to build workflows that combine AI capabilities with human judgment, clear boundaries, and rigorous verification.

What if AI could help you find flaws in software logic before they become costly bugs? This practical guide shows you how to use Claude Code alongside proven verification tools to test assumptions, uncover hidden issues and build confidence in your code. From first principles to production workflows, learn a smarter way to verify software with AI.

Abandona el ensayo y error en la IA generativa. Un marco práctico y metodológico basado en el falsacionismo de Karl Popper para auditar, estresar y dominar la interacción con modelos de lenguaje.

AI can help engineers ship code they cannot explain. This complete, illustrated guide helps engineering managers and CTOs grow judgement alongside that speed, with a six-session mentoring course, reusable exercises, and the business case for junior hiring.

Build real machine learning applications with TensorFlow.js, from your first model to production-ready systems. Learn how to train and deploy models in the browser and Node.js, tackle real-world challenges and explore modern techniques with practical JavaScript and TypeScript examples you can run and adapt.