Artificial Intelligence

Turn OpenAPI schemas into exhaustive test suites, wire Pact contracts between services, and let semantic fuzzers hunt the bugs random ones miss.

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.

 What You Will Learn1. Foundations of Creative WritingYou will explore the essential building blocks of storytelling, including plot development, character creation, dialogue writing, and narrative techniques.2. AI Tools and Prompt EngineeringThe book introduces you to modern AI writing tools and teaches you how to use them effectively through structured prompts and iterative refinement.3. Writing Different FormatsYou will learn how to create:Novels with strong plot and character arcsScreenplays with professional formattingPoetry with emotional depth and rhythmEngaging storytelling content across genres4. Editing and Quality ControlThe book emphasizes the importance of human editing, ensuring your work is polished, original, and aligned with professional standards.5. Publishing and MarketingA dedicated section guides you through publishing your work on platforms like Amazon KDP, including formatting, keywords, and marketing strategies.  Ethical and Responsible Use of AIOne of the most important aspects of this book is its focus on ethical writing. AI is a powerful tool, but it must be used responsibly. The book discusses:Avoiding plagiarismUnderstanding copyright ownershipMaintaining transparencyEnsuring originalityThis ensures that your work remains authentic and compliant with global publishing standards.  Who Should Read This Book?This book is ideal for:Students (BCA, MCA, BTech, and other disciplines)Aspiring authors and storytellersContent creators and bloggersScriptwriters and poetsEducators and researchersFreelancers and digital marketersNo prior experience with AI is required. The book starts from basics and gradually moves to advanced concepts.

Realistic ExpectationsThis book does not claim that AI will automatically guarantee success or eliminate all challenges. Instead, it presents AI as a support system that can enhance productivity when used correctly.Success depends on:Consistent effortProper implementationContinuous learningAdaptability to change A Long-Term Learning ResourceRather than a one-time read, this book is designed to be a practical reference guide. You can revisit different sections as your business grows and your needs evolve.  Final ThoughtArtificial Intelligence is not just for large corporations anymore. With the right approach, small businesses can also benefit from its capabilities.This book provides a structured, ethical, and practical pathway to understanding and applying AI—helping you take informed steps toward improving your business operations. 

Agentic Coding verspricht Geschwindigkeit – aber Vibe Coding liefert oft Chaos. EXACT Coding zeigt, wie Software-Craft-Prinzipien und TDD als Korrektiv wirken: für Code, der schnell entsteht und trotzdem hält, was er verspricht.

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

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。

Ready to move beyond the hype and build real applications with Large Language Models? Learn the practical patterns that work, straight from my own failures and successes. Using C# and Semantic Kernel, I'll show you how to craft AI applications that deliver value—not just demos. From enhancing LLMs with custom tools to orchestrating multi-agent systems, you'll discover proven approaches that scale. Skip the theory and start building production-ready AI features today.

What connects a mechanical calculator, a modern smartphone, the Internet, and artificial intelligence?Everything.The technologies that define today's digital world did not emerge independently. They are chapters in a much larger story—a story that begins with binary numbers and mechanical calculation and leads to machines capable of learning, generating content, understanding language, and taking action.The Code Revolution follows this extraordinary journey from early computation to intelligent machines.Discover how code became software, software became infrastructure, information became data, data became intelligence, and intelligence is now becoming increasingly autonomous.From Binary to Brains, this is the story of how computing changed the world—and where it may go next.

The book is free — pay-what-you-want with a $0 minimum. If you find it useful and want to contribute, you decide how much. If you can't or don't want to, just download and read without worry: the Creative Commons BY-SA 4.0 license even allows you to redistribute and modify it, as long as you credit the source and maintain the same license.

Understand how AI works, how we generate and evaluate evidence about its capabilities, and what it takes to make AI useful in the real world. An accessible, research-grounded exploration of the technology, its limitations and its practical impact.