Books

### Stop Analyzing Yesterday's Catastrophes. Calculate the Future in Absolute Real-Time. Traditional risk analysis architectures are fatally broken. While geopolitical crises, supply chain disruptions, and algorithmic market shifts unfold in milliseconds, corporate dashboards and governmental entities still rely on the "archaeology" of manual batch updates, committee meetings, and static PDF attachments. This technical dossier introduces the structural blueprint and core systems philosophy behind **Naciro**, a high-performance predictive inference engine written in C++. #### What you will find inside this 74-page architectural framework:* **The Architecture of Delay:** A deep-dive into the systemic latency gap between real-world events and analytical ingestion.* **Adversarial Ingestion:** Mechanics of how modern information warfare utilizes validation workflows to inject false narratives into corporate decision-making.* **The Crisis Logic:** Breaking down event telemetry into sub-second processing windows. *Note: This Leanpub publication serves as the official, open-access technical preprint and executive summary (the foundational opening chapters, first 73 pages) of the global manifesto "The Speed of Crisis". The book ends abruptly at a critical architectural cliffhanger.* 👉 **The complete, full-length 400+ page implementation manual can be found directly here:** https://nationfiles.com/en/amazon/

You already know how to build a Spring Boot service. Controllers, services, repositories, tests—it’s muscle memory. But what happens when your product manager asks for “an AI assistant” instead of “another REST endpoint”?Spring Boot Meets AI shows you how to plug modern AI models into the Spring applications you’re already running, using Spring AI’s fluent clients and Boot starters. You’ll go from zero to “curlable” AI endpoints in a few lines of code, then build up to real features: chat, summarisation, data extraction into POJOs, RAG over your own docs, and AI workflows that can safely call your own Java code.No new language. No separate AI microservice nobody wants to maintain. Just Spring Boot, Spring AI, and a set of patterns you can take back to work on Monday

Containers without CNIs are like cars without roads—going nowhere fast. Unlock the secret to seamless container networking with this no-nonsense guide. From Docker Swarm to Kubernetes, learn how to choose, deploy, and master CNI plugins like a pro. No fluff. Just the tools to build networks that actually work.

This book introduces screenshot testing (also called visual or snapshot testing): an automated approach that records a reference image of the expected design, then compares each test run against it to detect visual changes. It’s a natural complement to functional UI tests, which verify behavior but not appearance. The book focuses on the fundamental principles that apply to all frameworks. It works through the questions teams actually face: when to use screenshot tests and when not to, what to verify, which frameworks and tools fit your workflow, how to generate tests from preview functions in Jetpack Compose, how to integrate tests into a CI/CD pipeline, and how to optimize the whole process. By the end, you’ll be able to choose the framework that best suits your project and build a safety net that catches visual bugs before release. Every concept is demonstrated with a demo mood tracker application, with all code available online.

You have used a cross-compiler. But have you ever built one? This book takes you from first principles to a production-matched, containerised GCC and Clang toolchain — the hands-on deep-dive that the embedded Linux world has been missing.

PYTHON COMPLETE GUIDE is a comprehensive, hands-on guide to learning Python from the ground up. Covering everything from core concepts lto advanced topics. This book empowers readers to develop real-world skills.

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After more than twenty years at IBM — including roles as Regional Technical Leader for Security Technologies and Tivoli Software across Central and Eastern Europe — I have seen many SOC modernization projects. Organizations consistently face the same dilemma: substantial existing investments in SIEM, SOAR, processes, and human expertise versus the urgent need to keep pace with AI-powered threats.Most vendors pushed rip-and-replace cloud solutions. I took a different path.The Brownfield Agentic SOC concept was born from the realization that modernization does not require replacement. It is possible — and often far more effective — to intelligently augment existing infrastructure with multi-level AI agents, dynamic business context, structured investigation, and strong governance, while preserving data sovereignty and human judgment.This book is intentionally practical and conceptual. Certain advanced techniques (self-healing patterns, zero-visible-downtime adaptation, tamper-evident intelligent audit) are subject to pending patent protection and are presented here at architectural level only.I wrote this book for three audiences:CISOs and SOC leaders in regulated industries seeking realistic modernizationSecurity architects responsible for next-generation operationsTeams that prioritize sovereignty, compliance, and human-AI collaborationMy goal is to provide a clear, actionable blueprint that respects real-world constraints while delivering measurable improvement.I hope this book inspires and equips you to build more resilient, intelligent, and sovereign Security Operations Centers.Zsolt L. Kocsis, M.Sc., MBA Associate Professor honoris causa Budapest University of Technology and Economics (BME) May 2026

Every tutorial gets you to "Hello World." This book gets you to production. In 26 years of building and consulting on enterprise systems, I've seen exactly where the gap is, this is the book I wish existed when I started. You'll understand not just how to write the code, but why real systems are built the way they are.

Part VIII — Mathematical AppendicesTo support learning, the book includes:Optimization methodsProbability referencePseudocode for all algorithmsReal-world datasets and examplesThis makes the book self-contained for academic courses and self-study. 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.

Pedagogical Features To ensure clarity and academic depth, each chapter includes:·        Conceptual Explanation: Theoretical context and motivation·        Mathematical Derivation: Step-by-step proofs and equations·        Algorithm Design: Pseudocode for each major algorithm·        Numerical Examples: Solved problems for classroom and self-practice·        Visual Illustrations: Graphical understanding of value functions and convergence·        Exercises and Research Notes: For deeper investigationThis structure makes the book equally useful for students learning the subject, teachers designing course material, and researchers developing new models. Why This Book Is Unique 1.      Mathematical Depth: Every equation is derived and explained, not merely presented.2.      Pedagogical Precision: Structured for both classroom teaching and independent study.3.      Balanced Approach: Covers both classical RL (Bellman, DP, Q-learning) and modern RL (DQN, PPO, Actor-Critic).4.      Research Orientation: Provides open problems, mathematical proofs, and advanced theoretical questions.5.      Language Clarity: Written in simple, academic English with minimal jargon.While most books treat RL as a subset of machine learning, this book presents RL as a pure mathematical science of decision-making under uncertainty.

Mathematics of Reinforcement Learning: From Bellman Equations to Q-Learning  VOL-1 A Mathematical Journey through Dynamic Programming and Optimal Decision-Making Author: Anshuman Mishra, M.Tech (Computer Science) Assistant Professor, Doranda College, Ranchi University  COPYRIGHT PAGE© 2025 Anshuman Mishra, M.Tech (Computer Science) All rights reserved.No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopying, recording, or otherwise—without the prior written permission of the author or publisher, except for brief quotations used in reviews, academic references, or scholarly works.First Edition: 2025 DISCLAIMER This book is designed to provide academic and research-based knowledge on Mathematics of Reinforcement Learning, including the principles of dynamic programming, Bellman equations, Q-learning, and related computational models. The information contained herein is intended solely for educational purposes for students, teachers, and researchers in computer science, mathematics, and artificial intelligence.While every effort has been made to ensure the accuracy of the contents, the author and publisher make no representations or warranties with respect to the accuracy or completeness of the contents of this book. The examples, algorithms, and derivations have been thoroughly checked, but errors may still exist. The author and publisher shall not be liable for any damages arising from the use of the material contained herein.The mathematical examples and algorithms are for educational and illustrative purposes only. Readers implementing algorithms for research or practical projects are encouraged to verify results independently and consult additional resources as needed.All trademarks, trade names, or logos mentioned belong to their respective owners. Any resemblance of examples or case studies to actual data, individuals, or organizations is purely coincidental.  BOOK DESCRIPTION Title: Mathematics of Reinforcement Learning: From Bellman Equations to Q-Learning  VOL-1 Subtitle: A Mathematical Journey through Dynamic Programming and Optimal Decision-Making Author: Anshuman Mishra, M.Tech (Computer Science) Assistant Professor, Doranda College, Ranchi University About the Book The 21st century marks a revolutionary transformation in artificial intelligence (AI), where machines are not only learning from data but are also learning how to act intelligently in dynamic environments. Among the various branches of AI, Reinforcement Learning (RL) stands as the mathematical and conceptual foundation that allows computers and robots to make autonomous decisions through trial and reward.This book, Mathematics of Reinforcement Learning, serves as a bridge between mathematical theory and practical algorithms, enabling readers to deeply understand the mathematical intuition behind learning systems that think, adapt, and optimize behavior.Unlike traditional AI books that focus only on algorithmic implementation, this book unfolds the complete mathematical foundation—from Bellman equations and dynamic programming to Monte Carlo methods, temporal-difference learning, and Q-learning. Each topic is mathematically derived, systematically explained, and complemented with step-by-step numerical examples and proofs.This book is written specifically for:·        Undergraduate and postgraduate students (B.Tech, BCA, MCA, M.Sc. AI, Data Science)·        Teachers and researchers in artificial intelligence and applied mathematics·        Industry professionals and developers seeking deeper theoretical clarity in RL Philosophy Behind the Book Most introductory books on reinforcement learning explain algorithms but rarely delve into why these algorithms work or how their mathematical properties guarantee convergence, stability, and optimality. This book aims to unveil the mathematics that drives intelligence, presenting reinforcement learning not as a set of black-box algorithms but as a beautifully structured mathematical framework grounded in linear algebra, probability, optimization, and dynamic programming.Each chapter begins with fundamental theory and builds toward algorithmic application, showing how every step—from expectation computation to Bellman optimization—can be rigorously formulated using mathematical logic.The goal is to empower readers to not only use reinforcement learning but to understand and innovate upon it. Structure and Organization This book is divided into seven modules and twenty comprehensive chapters, organized in an intuitive learning sequence. Module I: Foundations of Reinforcement Learning It begins with the basic building blocks—agents, environments, states, actions, and rewards—and introduces readers to the concept of learning through interaction. Chapters 1 to 3 explore:·        The mathematical definitions of Markov Processes and Decision Models·        The essential linear algebra and probability theory underlying reinforcement learning·        The formal structure of Markov Decision Processes (MDPs) and Bellman equationsBy the end of this module, the reader understands the theoretical backbone of RL, paving the way for algorithmic exploration. Module II: Bellman Equations and Dynamic Programming Here, the mathematics of optimality takes center stage. The Bellman equations are explored in full depth—both expectation and optimality formulations—along with proofs of convergence and computational methods.Dynamic programming methods such as policy evaluation, policy iteration, and value iteration are introduced with complete derivations and worked-out numerical examples. The connection between dynamic programming and reinforcement learning is clearly established, showing how each step in the algorithm emerges from a recursive mathematical structure. Module III: Monte Carlo and Temporal-Difference Learning This module blends probability, sampling, and prediction. It explains how learning can happen from experience through Monte Carlo estimation and Temporal Difference (TD) learning. Readers learn the relationships between bias, variance, convergence speed, and data efficiency. The transition from offline to online learning is demonstrated through examples like the Blackjack problem and Random Walk prediction.Eligibility traces and TD(λ) methods are explained rigorously with mathematical equivalence proofs, bridging theory with implementation. Module IV: Control Algorithms — From Sarsa to Q-Learning The heart of reinforcement learning—learning to control—is covered in this section. Starting with on-policy control (Sarsa) and progressing to off-policy control (Q-Learning), readers explore the mathematical mechanisms that enable agents to learn optimal strategies.The derivation of the Q-learning update rule from the Bellman optimality principle is shown step-by-step, providing a strong conceptual understanding of how agents converge to optimal policies. Comparisons between different approaches (Sarsa, Expected Sarsa, and Q-Learning) are backed with numerical and graphical examples.    Module V: Advanced Mathematical Tools and Extensions At this point, the book transitions from classical reinforcement learning to advanced formulations. Topics include:·        Policy Gradient Theorem and its derivation·        Actor-Critic architecture with detailed gradient calculations·        Regularization and constrained optimization for safe and stable learning·        Entropy and KL-Divergence based formulations for robust policy optimizationReaders are introduced to Lagrangian optimization in RL, showing how constraints can be mathematically imposed to ensure balanced exploration and exploitation. Module VI: Deep and Approximate Reinforcement Learning This section connects traditional reinforcement learning to deep neural networks and function approximation. The mathematical underpinnings of Deep Q-Networks (DQN) are derived, explaining loss functions, gradient backpropagation, and the role of target networks.Advanced architectures such as Double DQN, Dueling Networks, Prioritized Replay, and Proximal Policy Optimization (PPO) are also presented with mathematical clarity. Through carefully designed examples, the book shows how deep learning integrates with reinforcement learning, resulting in modern AI systems like AlphaGo and autonomous robots. Module VII: Theoretical and Research Perspectives The final section consolidates all mathematical insights, focusing on proofs, convergence theorems, and future research directions. It contains:·        Rigorous proofs of TD and Q-learning convergence·        Stability analysis using stochastic approximation theory·        Exploration of open challenges such as safe RL, explainable RL, and quantum RLThis section encourages teachers and researchers to extend the theoretical boundaries of reinforcement learning. Pedagogical Features To ensure clarity and academic depth, each chapter includes:·        Conceptual Explanation: Theoretical context and motivation·        Mathematical Derivation: Step-by-step proofs and equations·        Algorithm Design: Pseudocode for each major algorithm·        Numerical Examples: Solved problems for classroom and self-practice·        Visual Illustrations: Graphical understanding of value functions and convergence·        Exercises and Research Notes: For deeper investigationThis structure makes the book equally useful for students learning the subject, teachers designing course material, and researchers developing new models. Why This Book Is Unique 1.      Mathematical Depth: Every equation is derived and explained, not merely presented.2.      Pedagogical Precision: Structured for both classroom teaching and independent study.3.      Balanced Approach: Covers both classical RL (Bellman, DP, Q-learning) and modern RL (DQN, PPO, Actor-Critic).4.      Research Orientation: Provides open problems, mathematical proofs, and advanced theoretical questions.5.      Language Clarity: Written in simple, academic English with minimal jargon.While most books treat RL as a subset of machine learning, this book presents RL as a pure mathematical science of decision-making under uncertainty.

Stop treating AI like a chatbot. Learn to exploit Prompt Injections, automate OSINT, write evasive payloads, and build autonomous defensive agents in this hands-on technical manual.

As the author, I (Anshuman Mishra) have written this book with the spirit of mentorship — not just to explain how chatbots work, but to help you build one confidently and ethically. I have taught AI, programming, and computer science for nearly two decades, and I have seen countless students struggle to bridge the gap between theory and implementation.This book closes that gap. It teaches you what to do, why to do it, and how to do it right. It’s not just a manual — it’s a journey from curiosity to mastery.You are not just learning to build a chatbot; you are learning to create intelligence — responsibly, creatively, and with purpose.

As the author, I (Anshuman Mishra) have written this book with the spirit of mentorship — not just to explain how chatbots work, but to help you build one confidently and ethically. I have taught AI, programming, and computer science for nearly two decades, and I have seen countless students struggle to bridge the gap between theory and implementation.This book closes that gap. It teaches you what to do, why to do it, and how to do it right. It’s not just a manual — it’s a journey from curiosity to mastery.You are not just learning to build a chatbot; you are learning to create intelligence — responsibly, creatively, and with purpose.