Books

 Closing Thoughts“Mathematics for Artificial Intelligence – II (Statistics and Optimization)” is more than a textbook—it is a guidebook for mastering the mathematics behind AI. While Volume I laid the foundation and your other book covered data science statistics, this volume pushes students, researchers, and practitioners into the advanced territory where modern AI thrives.Whether you want to become a machine learning engineer, AI researcher, data scientist, or academic scholar, mastering the material in this book will give you the edge to not only use AI tools but also innovate and push the boundaries of artificial intelligence.

Turn "our Shopware store feels slow" into a measurable 30-day plan: one fix a day, each with a before/after metric. Field-tested engineering on caching, Elasticsearch, Redis and CDN — not generic tips.

Benefits of Studying This Book 1. Deep Conceptual Understanding You will understand why AI algorithms work, not just how to run them. This allows you to innovate, debug, and improve models. 2. Career Advantage Strong mathematical foundations make you stand out in interviews for AI, ML, and DS roles. Many recruiters test candidates on linear algebra and probability skills. 3. Research Readiness Postgraduate students and researchers can directly apply these mathematical tools to design and analyze experiments. 4. Practical AI Skills Python-based implementation examples ensure that you can directly apply mathematical concepts in real-world AI systems. 5. Interdisciplinary Edge Mathematics learned here is not limited to AI — it can be applied in robotics, quantum computing, finance, bioinformatics, and more. How This Book Helps After StudyAfter completing this book, you will be able to:·        Build AI models from scratch, knowing exactly what mathematical operations are happening inside.·        Optimize models for performance using a deep understanding of linear algebra operations.·        Analyze and interpret model predictions probabilistically.·        Handle uncertainty and noise in datasets effectively.·        Implement advanced AI concepts like PCA, SVD, Bayesian inference, and Markov models without relying solely on pre-built libraries.This knowledge will directly help in:·        Academics: Scoring well in AI/ML/DS university courses.·        Industry: Working as an AI engineer, data scientist, ML engineer, or research scientist.·        Competitive Exams: Preparing for GATE, NET, and other AI-related exams where mathematics is heavily tested.·        Research: Publishing papers where mathematical rigor is required to explain new AI techniques. 

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WHO SHOULD READ THIS BOOK?This book is ideal for:·        BCA, MCA, B.Tech, M.Tech students·        UGC NET aspirants·        AI/ML researchers·        Data scientists·        AI developers·        University professors·        PhD scholars·        Industry professionals working with black-box models·        Anyone who wants mathematical clarity on XAIIts writing style balances mathematical rigor with readability, making it useful for self-study and classroom use. TEACHING & LEARNING BENEFITS·        50+ diagrams, proofs, and mathematical derivations.·        Step-by-step logical flow for each model.·        Case studies from healthcare, finance, law, and engineering.·        Practical coding references (without over-reliance on tools).·        Integration of statistics, calculus, causality, and deep learning.·        Real-world examples for intuitive understanding.·        Problems at the end of each chapter (optional addition).Instructors can adopt this book for academic courses in:·        Explainable AI·        Machine Learning·        Statistical Inference·        Causality·        Artificial Intelligence Foundations·        Deep Learning Interpretability UNIQUE CONTRIBUTIONS OF THIS BOOKUnlike other XAI or ML books, this work by Anshuman Mishra offers:·        Mathematical derivations for SHAP, IG, LIME, and other explainability tools.·        Original proofs for fairness properties in attribution methods.·        Detailed causal diagrams and do-calculus explanations.·        A structured approach to XAI evaluation metrics.·        Coverage of transformer explainability—rare in academic books.·        Clarity in blending classical mathematical theory with modern AI systems.This makes the book a reference-level resource for the next decade of AI learning.

WHO SHOULD READ THIS BOOK?This book is ideal for:·        BCA, MCA, B.Tech, M.Tech students·        UGC NET aspirants·        AI/ML researchers·        Data scientists·        AI developers·        University professors·        PhD scholars·        Industry professionals working with black-box models·        Anyone who wants mathematical clarity on XAIIts writing style balances mathematical rigor with readability, making it useful for self-study and classroom use. TEACHING & LEARNING BENEFITS·        50+ diagrams, proofs, and mathematical derivations.·        Step-by-step logical flow for each model.·        Case studies from healthcare, finance, law, and engineering.·        Practical coding references (without over-reliance on tools).·        Integration of statistics, calculus, causality, and deep learning.·        Real-world examples for intuitive understanding.·        Problems at the end of each chapter (optional addition).Instructors can adopt this book for academic courses in:·        Explainable AI·        Machine Learning·        Statistical Inference·        Causality·        Artificial Intelligence Foundations·        Deep Learning Interpretability UNIQUE CONTRIBUTIONS OF THIS BOOKUnlike other XAI or ML books, this work by Anshuman Mishra offers:·        Mathematical derivations for SHAP, IG, LIME, and other explainability tools.·        Original proofs for fairness properties in attribution methods.·        Detailed causal diagrams and do-calculus explanations.·        A structured approach to XAI evaluation metrics.·        Coverage of transformer explainability—rare in academic books.·        Clarity in blending classical mathematical theory with modern AI systems.This makes the book a reference-level resource for the next decade of AI learning.

4. Who Should Read This Book?This book is specially designed for a wide audience:4.1 StudentsStudents of:Artificial intelligenceData scienceComputer scienceInformation technologyOperations researchApplied mathematicswill find this book essential for understanding foundations and applications of intelligent decision-making.4.2 ResearchersThis book helps researchers explore:Decision-making modelsPlanning algorithmsRisk-aware AIMathematical modelingOptimization under uncertaintyIt helps form a strong base for research projects and PhD work.4.3 Industry ProfessionalsEngineers and developers working on:RoboticsAutonomous vehiclesDecision support systemsPredictive analyticsAI toolsFinancial modelingwill find the algorithms, pseudocode, and frameworks highly practical.4.4 Faculty MembersTeachers and professors can use this book as:A primary textbookA reference guideA source of problems and case studiesA foundation for graduate and research courses 5. Learning OutcomesAfter studying this book, readers will be able to:Understand and construct utility functionsEvaluate rational choices under uncertaintyBuild decision treesConstruct influence diagramsDesign sequential decision systemsFormulate and solve MDPsApply POMDPs to real problemsImplement classical planning algorithmsModel multi-agent interactions using game theoryApply Bayesian decision theory to uncertain environmentsUnderstand the foundation of reinforcement learningBuild real-world decision and planning systemsThis ensures comprehensive mastery of both theory and practice.

Most enterprise LLM deployments fail not at the demo stage — but at scale. This book gives AI architects and engineering leaders the cost models, architecture patterns, and fine-tuning frameworks to run LLMs reliably in production, sized for your actual organization.

[ONE HARDWARE CHIP. ONE MOBILE APP. ZERO LATENCY.] 🚀Ever tried to stream a continuous high-resolution video flux from a microcontroller straight to an Android device without your heap crashing?Most tutorials give you generic code that breaks the second you step into the real world. I built a system for a live, bustling beauty salon environment where failure wasn't an option.Introducing Building the KDO Virtual Mirror—a step-by-step engineering roadmap that bridges C++ on the ESP32-WROVER with advanced Java ViewModels on Android. No third-party network libraries. No fluff. Just raw TCP server sockets, low-level OV5640 camera registry tuning, and lightning-fast pixel streaming.From a simple blink app to multi-core dual-tasking and pinch-to-zoom gestures—this is how you build production-grade IoT.📦 Dropping soon on Leanpub. Are you ready to master the dual-ecosystem chain?

A typed, signed-deliverable playbook for the enterprise-scale Oracle 19c → Azure migration. Across fifteen chapters, thirteen signed JSON deliverables chain end-to-end from Migration Assessment Bundle to Decommissioning Readiness Report. Defensible at a code review. Sign-off-ready for a director.

Escrito por um líder de engenharia que constrói sistemas de IA em escala, este playbook traduz o que realmente está acontecendo por trás do hype em decisões que você pode tomar hoje. 18 capítulos cobrindo fundamentos de IA, aplicações de negócio, avaliação de fornecedores, segurança, governança e seu plano de ação de 90 dias. Sem código, sem jargão.

Written by an engineering leader who builds AI systems at scale, this playbook translates what's really happening behind the hype into decisions you can make today. 18 chapters covering AI fundamentals, business applications, vendor evaluation, security, governance, and your 90-day action plan. No code, no jargon — just clarity.

Guia prático para desenvolvimento agêntico com Claude Code, MCP, Claude API e Agent SDK. De modelos mentais a arquiteturas de produção — tudo que um dev experiente precisa para trabalhar de forma agêntica, não apenas usar ferramentas de IA.

A hands-on guide to building with Claude Code, MCP, the Claude API, and the Agent SDK. From mental models to production architectures — everything an experienced developer needs to work agentically, not just use AI tools.

### Die Krise tickt in Sekunden. Ihre Analyse tickt in Stunden. Das ist das eigentliche Risiko. Moderne Risiko- und Lageberichte in Unternehmen und Ministerien sind historische Dokumente. Sie zeigen eine Momentaufnahme der Vergangenheit, die so formatiert ist, dass sie wie die Gegenwart aussieht. Wenn eine Lieferkette kollabiert oder eine Desinformationskampagne Märkte manipuliert, agieren Algorithmen in Millisekunden – während menschliche Krisenstäbe noch auf Freigabeschleifen warten. Dieses technologische Dossier dokumentiert das systemtechnische und deterministische Fundament hinter dem Forschungsprojekt **NationFiles** und der Echtzeit-Inferenz-Engine **Naciro**. #### Inhalt des 84-seitigen Frameworks:* **Die Anatomie der Latenz:** Warum unsere Informationsketten genau in dem Moment brechen, in dem es darauf ankommt.* **Die Zersplitterung der Wahrheit:** Wie asynchrone Uhren zwischen Staat, Firma und Markt Desinformationskorridore schaffen.* **Deterministisches Engineering:** Der radikale Wechsel von retrospektiver Berichterstattung zu prädiktiver Kausalitätsberechnung. *Hinweis: Diese Publikation stellt das offizielle wissenschaftliche Whitepaper und die Executive Summary (die ersten 84 Seiten) des umfassenden Fachbuch-Manifests „Die Geschwindigkeit der Krise“ dar. Der Text endet absichtlich mitten in einer hochspannenden geopolitischen Szenario-Analyse.* 👉 **Das vollständige, über 400 Seiten starke Fachbuch-Manifest direkt bestellen unter:** https://nationfiles.com/de/amazon/