Books

AI can write code faster than you can read it—so why do projects still derail? The answer is the spec. This book teaches Spec-Driven Development hands-on, building a complete app one loop at a time with Spec Kit. Stop prompting and praying. Start shipping software AI actually gets right.

45% of AI-generated code has vulnerabilities. This book covers prompt injection, data leakage, secure coding with AI tools, MCP server hardening, agent sandboxing, supply chain security, and OWASP LLM compliance. 14 chapters with attack demos and defense code.

In Deepseek AI, we delve into the transformative events of January 27, 2025, that reshaped our world. Discover how Deepseek's emergence has disrupted traditional capitalism, giving rise to Technofeudalism and igniting a new cold war.

In Deepseek AI, we delve into the transformative events of January 27, 2025, that reshaped our world. Discover how Deepseek's emergence has disrupted traditional capitalism, giving rise to Technofeudalism and igniting a new cold war.

In Deepseek AI, we delve into the transformative events of January 27, 2025, that reshaped our world. Discover how Deepseek's emergence has disrupted traditional capitalism, giving rise to Technofeudalism and igniting a new cold war.

You leave Claude Code running and come back to something missing. This book is arranged by the accident, not the mechanism: databases dropped, files deleted, git history rewritten, the rewind command taking work with it. Three of the four get the exact hook that blocks the command before it runs, with the recovery if it already happened; no PreToolUse hook sees the rewind command, and the book gives the settings-level lever and names the one place a refusal could still live. Appendix B prints all 195 commands the guards were fired at, refusals and allowances both.

Ihre Use Cases haben einen neuen Leser: einen KI-Agenten, der sie direkt in Code und Tests umwandelt. Die Implementierung ist mittlerweile kostengünstig, doch die präzise Formulierung der Absicht stellt den Engpass dar. Dieser Leitfaden vermittelt Ihnen, wie Sie Use Cases verfassen, die sowohl für Stakeholder und Ingenieure als auch für die KI-Agenten, die sie umsetzen, funktionieren.

Graphs are everywhere.From social media networks and recommendation systems to autonomous vehicles, cybersecurity platforms, and modern artificial intelligence, graph structures have become the language of connected data.But how do machines understand relationships?How do search engines rank billions of pages?How do recommendation systems predict what users will like next?How do AI systems learn from complex networks?Graph Theory with AI Applications: Foundations, Algorithms, and Modern Neural Approaches (VOL-1) provides the answers.This book takes readers on a structured journey through graph fundamentals, graph algorithms, shortest path methods, network optimization, social network analytics, community detection, and graph mining techniques. Designed for students, researchers, educators, and professionals, it combines mathematical foundations with practical AI applications.If you want to understand the science behind connected intelligence and prepare yourself for the future of Graph Neural Networks and AI-driven graph learning, this book is your starting point.Discover the foundations. Master the algorithms. Build the future of Graph AI.

What happens when intelligent agents must negotiate, cooperate, compete, and learn simultaneously?Welcome to the advanced frontier of Artificial Intelligence.Game Theory and Artificial Intelligence (VOL-2) explores how modern AI systems make strategic decisions in environments filled with uncertainty, multiple objectives, competing interests, and dynamic interactions.Inside this volume, readers will discover:✓ Nash Q-Learning and Strategic Reinforcement Learning✓ Deep Multi-Agent Reinforcement Learning (MARL)✓ DQN, Actor-Critic, MADDPG, QMIX, and CTDE Frameworks✓ Mechanism Design and Incentive Engineering✓ Decision Making Under Uncertainty✓ Social Choice Theory and Collective Intelligence✓ Game-Theoretic Machine Learning✓ Robotics, Cybersecurity, Economics, and Communication Networks✓ Evolutionary Game Theory and Learning Dynamics✓ Quantum Game Theory and Future AI Research✓ AI Safety, Alignment, and GovernanceFrom autonomous vehicles and intelligent robots to cybersecurity defense systems and large-scale AI simulations, this book provides the theoretical and practical foundations necessary to understand the future of strategic artificial intelligence.Whether you are a student, researcher, engineer, or AI professional, this volume offers a roadmap to some of the most exciting and influential areas of modern AI research.

Every intelligent system faces a fundamental challenge:How do you find the best solution when there are millions—or even trillions—of possibilities?From A* search and heuristic optimization to neural architecture search, hyperparameter tuning, constraint satisfaction, and quantum optimization, modern AI depends on sophisticated strategies for navigating combinatorial search spaces.In this advanced second volume, Anshuman Mishra explores the powerful algorithms that enable intelligent systems to search smarter, optimize faster, and scale beyond brute force computation.Discover how combinatorial thinking drives machine learning, optimization, quantum AI, and the future of intelligent decision-making.The future of AI belongs to those who understand combinatorial complexity.

Artificial Intelligence is not merely about data, learning, or neural networks—it is fundamentally about exploring vast spaces of possibilities.Every search algorithm, planning system, optimization framework, recommendation engine, and machine learning model faces a common challenge: navigating an enormous combinatorial universe of potential solutions.In Combinatorial Thinking in Artificial Intelligence, Anshuman Mishra reveals the mathematical foundations that drive modern AI systems. Through permutations, combinations, graph theory, probability, search strategies, and optimization techniques, readers learn how intelligent systems reason, search, and make decisions in complex environments.If you want to understand why AI algorithms work—not just how to implement them—this book provides the missing mathematical perspective.Discover the combinatorial engine behind intelligence.

Explore the advanced frontier of Category-Theoretic Artificial Intelligence. Discover how transformers, symbolic reasoning, reinforcement learning, higher categories, topos theory, and compositional learning can be unified through the powerful language of category theory. An essential guide for researchers, AI professionals, and future AGI innovators.

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.

You’ll get a clear, beginner-friendly introduction to AI and machine learning, explanations of neural networks, data, and training processes, practical insights into how AI really works (without hype) and a hands-on PyTorch project to build your own small AI

Your company's answers are already written down. This book builds the machine that finds them.