Books

Platform engineering is changing fast, and AI is raising the stakes. This practical guide shows you how to build internal developer platforms that work in the real world, from cloud-native foundations to AI-powered workloads. Follow hands-on examples and a production-grade reference architecture to create platforms that are secure, scalable and built to evolve.

A structured five‑guide outdoor skills system designed for beginners who want to camp confidently, navigate safely, prepare for emergencies, and build essential bushcraft skills. Includes survival basics, navigation, firecraft, shelter building, and readiness checklists.

一本系统讲透"世界模型"的中文技术专著。从模型基强化学习、生成式视频模型、JEPA三大谱系出发，覆盖数学基础、核心算法、工程实现与前沿应用，带你理解下一代AI的核心范式。

La maggior parte delle aziende sostiene di essere agile. Poche lo sono davvero. I rituali sono cambiati, ma la mentalità “waterfall” è rimasta. Questa guida illustra come si presenta il vero approccio agile attraverso una storia concreta: un team autonomo che, in otto settimane, è passato da un problema aziendale a un impatto misurabile. Scoperta continua, TDD, feature flag, nessun story point. Pratiche che puoi mettere in atto già da lunedì.

"Jag Vet Inte Ännu, Men Jag Dansar Ändå" är en fängslande blandning av memoar och manifest som utforskar kroppens dolda intelligens, rörelsens läkande kraft och konsten att våga skapa i det okända. Genom att kombinera vetenskap, personliga anekdoter och röster från fem extraordinära kvinnor bjuder boken in till att släppa kontrollen och omfamna det kreativa kaoset. Det är en inbjudan att lita på kroppens intuition och sätta scenen i brand.

In parte memoria, in parte manifesto artistico, in parte cospirazione dietro le quinte contro la certezza. Una ballerina professionista esplora la propriocezione, il ritmo e il disagio del non sapere, e il motivo per cui la creatività funziona al meglio nel momento in cui smetti di cercare di controllarla.

Build Linux systems that do more than boot. This hands-on guide takes you from bootloader and kernel to secure updates, reproducible images, manufacturing and fleet operations. With runnable code and production-tested techniques, you’ll learn how to build Linux appliances designed for the real world.

本書では、Compose Compiler、Runtime、UI レイヤー、そしてパフォーマンスチューニングに至るまで、Jetpack Compose のすべてを深く掘り下げます。Jetpack Compose の内部ソースコードを一行ずつ読み解きながら、なぜその設計が選ばれたのか、その背景にある理由を明らかにし、皆さんが日々書いているコードと内部メカニズムを結びつけていきます。 本書を読み終える頃には、Compiler、Runtime、UI レイヤーがどのように連携して動作しているのか、その全体像を頭の中で描けるようになるでしょう。そして最終的には、その理解をもとに、日々開発している Compose プロジェクトのパフォーマンスを大きく改善できるようになります。 コース（英語）：Jetpack Compose Mechanisms | ハードカバー書籍（英語） | ISBN-13: 9798185354780

Move beyond AI demos and build agents that can actually run IT operations. This practical guide takes you from MCP fundamentals to production-ready monitoring, incident response, remediation, Kubernetes operations, security and multi-agent systems, with complete runnable code and a strong focus on safe autonomy.

Cyber threats do not stop at the office network. When industrial systems are targeted, the consequences can reach far beyond lost data. This practical guide shows how to secure OT and critical infrastructure from the ground up, combining proven standards, real-world defenses and hands-on guidance for the systems that keep the world running.

A cinematic, hybrid‑illustrated survival guide featuring eight printable wallet‑size reference cards. Includes knots, fire‑starting basics, emergency signals, navigation, packing essentials, and lost/stranded protocols.

门禁、支付、定位、摄像和个人 AI 都证明她到过命案现场。她必须先证明，这五个证人从来不是五个。

A cinematic, illustrated sustainability guide designed for beginners, renters, families, and small‑space homes. Includes eco habits, reusable swaps, composting options, home audits, energy optimization, renter‑friendly upgrades, and a 7‑day activation plan.

Build powerful web applications with Python and Django.Django is one of the most capable frameworks for building modern web applications, but mastering it requires more than learning a few commands. You need to understand its architecture, database layer, request-response cycle, security model, APIs, testing practices, performance techniques, and deployment workflow.Django Unleashed: Building High-Performance Web Applications with Python provides a practical roadmap for developing Django applications from the ground up.Starting with Django fundamentals, you will learn how projects and applications are structured, how URLs connect requests to views, how templates generate dynamic interfaces, and how Django's ORM communicates with databases.The book then moves into models, QuerySets, relationships, forms, validation, authentication, authorization, static files, and media handling.A dedicated section on Django REST Framework introduces REST APIs, serializers, APIViews, ViewSets, routers, authentication, CRUD operations, filtering, pagination, and testing. You will also explore frontend integration using HTML, CSS, JavaScript, AJAX, React, and Vue.js.As your skills develop, the book introduces professional development practices including automated testing, CI/CD, security hardening, database optimization, caching, Redis, Memcached, Celery, profiling, and application scaling.The deployment chapters explain how to prepare Django applications for production and work with technologies such as Nginx, Gunicorn, HTTPS, AWS, DigitalOcean, and Heroku.With practical examples, troubleshooting guidance, real-world development concepts, and a comprehensive reference section, this book is designed to help you move from learning Django to building applications that are ready for real-world use.Learn Django. Build applications. Secure your code. Optimize performance. Deploy with confidence.

Statistics is the language of uncertainty. AI is the science of learning from data. Together, they provide a powerful foundation for modern intelligent systems.Applied Statistics with AI: Hypothesis Testing and Inference for Modern Models is designed for learners who want to understand how statistical methods can be applied to Artificial Intelligence, Machine Learning, Data Science, and modern research.The book begins with essential statistical foundations, including data types, sampling, preprocessing, descriptive statistics, visualization, probability, and probability distributions. It then builds toward statistical inference, covering estimation, Maximum Likelihood Estimation, Bayesian estimation, hypothesis testing, p-values, confidence intervals, Type I and Type II errors, and statistical significance.Readers will learn how classical statistical tests such as t-tests, ANOVA, and Chi-Square tests can be used in AI-related contexts, along with non-parametric methods such as Wilcoxon, Mann-Whitney, Kruskal-Wallis, and Kolmogorov-Smirnov tests.The book then connects statistics directly to Machine Learning through regression, model evaluation, cross-validation, resampling, feature selection, PCA, regularization, A/B testing, and statistical power.Advanced chapters explore Bayesian inference, MCMC, causal inference, uncertainty quantification in deep learning, and confidence estimation in AI predictions. Real-world case studies demonstrate how statistical inference can support applications in healthcare, finance, Natural Language Processing, and Computer Vision.The book also addresses an increasingly important dimension of AI: responsible statistical practice. Readers will explore bias detection, fairness, ethical hypothesis testing, and the responsible interpretation of AI research results.Finally, the book looks ahead to automated statistical inference, AI-driven hypothesis generation, and emerging research challenges.Whether you are a student learning statistics for AI, a researcher evaluating machine learning experiments, a data scientist analyzing evidence, or an AI practitioner seeking stronger statistical foundations, this book provides a structured path from fundamental concepts to modern applications.Understand the data. Test the hypothesis. Quantify uncertainty. Make better AI decisions.