Books

Pedagogical Highlights ·        Illustrations and Diagrams: Each topic is accompanied by clear, labeled figures showing transformations, kinematic chains, and algorithmic workflows.·        Mathematical Derivations: Detailed step-by-step derivations of equations — from rotation matrices to dynamic equations of motion.·        Conceptual Summaries: Every chapter concludes with key takeaways and conceptual summaries to reinforce learning.·        Case Studies and Exercises: Includes practical assignments and research-oriented projects to inspire deeper exploration.·        Interdisciplinary Connection: Bridges the gap between mechanical design, control systems, and artificial intelligence through unified modeling. Intended Audience ·        Engineering Students — especially from Computer Science, Electronics, Mechanical, and Mechatronics backgrounds.·        MCA/M.Tech Students specializing in AI, Data Science, or Automation.·        Researchers working on intelligent control, robotics simulation, or human-robot collaboration.·        Industry Professionals seeking to understand how AI can enhance robotic modeling and performance.·        Faculty Members developing new courses or reference material in Robotics and Artificial Intelligence. Educational and Research Impact This book is not just a compilation of topics; it is a comprehensive educational framework. Each chapter is designed to act as a mini research guide, encouraging experimentation, simulation, and publication.The author’s academic experience of over 18 years brings an authentic balance of teaching methodology and research insights. Students will gain confidence in deriving equations, implementing algorithms, and developing hybrid AI-robotic systems. Future Outlook The future of robotics lies in adaptability — machines that learn from their surroundings and optimize their actions dynamically. With advances in quantum computing, neural hardware, and real-time AI systems, the mathematical models explored in this book will form the foundation for the next generation of intelligent machines.From autonomous drones to AI-driven robotic surgeons, the applications are endless, and all of them depend on the same universal principles — mathematics and intelligence.This book will help its readers not only understand these principles but also innovate upon them.

The future of robotics lies in adaptability — machines that learn from their surroundings and optimize their actions dynamically. With advances in quantum computing, neural hardware, and real-time AI systems, the mathematical models explored in this book will form the foundation for the next generation of intelligent machines.From autonomous drones to AI-driven robotic surgeons, the applications are endless, and all of them depend on the same universal principles — mathematics and intelligence.This book will help its readers not only understand these principles but also innovate upon them.

7. Why and How This Book is Important for Study 7.1 Why Important ·        It bridges theory and practice—unlike most ML books that focus only on coding, this book explains the deep mathematical backbone.·        It ensures readers understand tensors beyond black-box usage, enabling creativity and innovation in AI model design.·        It provides a unified approach to tensor calculus across multiple AI domains: vision, NLP, reinforcement learning, and multimodal AI. 7.2 How Important ·        Students gain confidence in handling multidimensional data.·        Researchers learn new techniques for model optimization and tensor decompositions.·        Practitioners can improve model efficiency, scalability, and interpretability.·        Educators can use the book as a curriculum resource for advanced AI/ML courses.

From the broken boy racing on dusty tracks to prove he mattered, to the visionary doctor who built a hospital where the poor are treated like kings—Doctor Number One is an unforgettable saga of ambition, love, and redemption.One man’s white-hot drive to be first became the force that saved thousands.A story of scalpel-sharp ambition and the grace that heals both patients and the healer himself.

Modern cybersecurity is no longer just about firewalls and antivirus. It is about architecture, governance, secure software delivery, cloud resilience, Zero Trust, AI security, and operational discipline.The Master Guide to Cyber Security brings these domains together into one practical enterprise-focused reference designed for modern security professionals, architects, engineers, and technology leaders.Built around real-world frameworks, secure-by-design principles, and current threat realities, this guide provides a structured roadmap for building secure systems in cloud-native and enterprise environments.

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A short course book on the classical theory of the method of characteristics for first-order partial differential equations, taking you from first principles to explicit solutions in twenty pages, and working through every example step by step.

·        Interdisciplinary Approach: Unlike standard AI textbooks, this book integrates pure mathematics (topology, geometry) with applied machine learning in a unified framework.·        Balanced Pedagogy: It is designed to be accessible for advanced undergraduates while still being valuable for postgraduates and researchers.·        Rich Case Studies: From image recognition to drug discovery, the book demonstrates how topology and geometry directly influence AI applications.·        Future-Oriented: The content prepares readers for the next decade of AI, where geometric deep learning and topological methods will likely play a central role. Who Should Read This Book·        Undergraduate and Postgraduate Students: Especially in computer science, data science, AI, mathematics, and applied physics.·        Researchers: Those working on machine learning theory, computer vision, NLP, bioinformatics, or robotics.·        Industry Professionals: AI engineers who wish to understand advanced techniques in representation learning and geometric deep learning.·        Educators: Professors and instructors seeking a comprehensive resource for teaching geometry and topology in AI. Applications in Real-World AI1.     Computer Vision: Understanding 3D object recognition, mesh learning, and geometric invariances.2.     Natural Language Processing: Hyperbolic embeddings for hierarchical representations (e.g., knowledge graphs, semantic structures).3.     Bioinformatics and Healthcare: Protein structure analysis, drug discovery, and medical imaging.4.     Social Network Analysis: Graph neural networks for influence prediction, fraud detection, and recommender systems.5.     Robotics and Control: Path planning on manifolds, geometric optimization for robot movement.6.     Financial Modeling: High-dimensional data analysis for risk detection and market prediction. Future DirectionsThe book also emphasizes future research avenues, including:·        Topology-aware deep learning architectures·        Hyperbolic and spherical embeddings for AI·        Integrating physics and geometry with machine learning·        Interpretability and robustness through topological analysis·        Next-generation AI systems designed with geometric intelligence   

What if the dreaded Lord of Death retired… and the heavens held interviews to replace him?In a cosmic HR spectacle filled with divine drama, 18 of India’s most beloved spiritual voices—from stern non-dual masters to ecstatic bhajan-singing gurus—compete for the ultimate job: becoming the new Yamraj. Expect sandalwood-scented Yamduts, yoga-practicing buffaloes, and death transformed from terror into a conscious, graceful celebration.

By diving into this book, students will:1.     Master Supervised Learning Fundamentals Gain strong conceptual understanding and practical competence.2.     Evate & Deploy Models Effectively Understand how to build, validate, interpret, and deploy high-quality models.3.     Think Critically about AI Systems Develop ethical awareness and critical reasoning regarding data bias and model behavior.

Ви п'єте мате. Знаєте, що там насправді? Три кофеїноактивні молекули, а не одна. Чотири протоколи, що дають чотири хімічно різні напої з однієї трави. Родина з п'яти кофеїновмісних рослин, яку решта світу ледь помітила. Це робочий посібник — для тих, хто стежить за хімією, називає обладнання своїми іменами, розрізняє регіони і не дозволяє маркетинговій бляшанці видавати себе за *Ilex paraguariensis*. Калабаса й бомбілья в Буенос-Айресі. Куя й бомба в Порту-Алеґрі. Гуампа й ріг в Асунсьйоні. Французький прес у Берліні. Та сама рослина — чотири протоколи, вимірювально різні напої. Якщо ви колись дивувалися, чому мате діє інакше, ніж кава; чому Аргентина п'є його гірким, а Бразилія — зеленим; що насправді означають *деспалада* і *con palo*; чому Meta Mate Viola пахне вишнею і тютюном; або що тихо будують у Місьйонесі українські родини за Kalena і Rojo Especial — ця книжка відповідає в деталях. Вісімнадцять розділів. Бренд-атлас. З джерелами. Для характерників. Без велнес-туману.

The AI revolution presents one of the most significant entrepreneurial frontiers in history. Yet, technology alone will not define the leaders of tomorrow—it will be the fusion of innovation, strategic execution, ethical responsibility, and visionary leadership.“AI-Powered Entrepreneurship: Innovate, Scale, and Lead the Future” invites you to become part of this transformation. Whether you are crafting your first business plan or steering a scaling enterprise into new AI-driven markets, the knowledge, frameworks, and tools within this book will equip you to make informed decisions, seize emerging opportunities, and contribute meaningfully to the evolving global economy.

A Sample Learning Journey with This Book Imagine a final-year MCA student who needs to select a project topic.·        After Chapter-3, they can identify a novel, research-worthy problem.·        By Chapter-5, they will know how to collect, clean, and preprocess relevant data.·        Using Chapter-6 and 8, they can implement a fair and unbiased ML model.·        Through Chapter-9 and 10, they can interpret results with statistical confidence.·        By Chapter-11, they will have the skills to write a publication-ready paper.In short, the book transforms a student project into publishable research.

·        Comprehensive Learning Path: The book starts with basics and gradually leads you to advanced topics, making it accessible for beginners and challenging for advanced learners.·        Contextual AI Applications: Every concept is illustrated with AI and ML examples, ensuring relevance and immediate applicability.·        Enhanced Understanding of AI Models: Knowing data structures like trees and graphs clarifies how decision trees or knowledge graphs operate internally, boosting your model-building skills.·        Algorithm Efficiency Awareness: Understanding algorithm complexity and heuristics allows you to write optimized AI programs that can handle large datasets and real-time processing.·        Practical Coding Exercises: With implementations in Python, you will develop a coding mindset essential for AI practitioners.·        Preparation for Research and Development: The book equips you to contribute to AI research and innovate new algorithms or improve existing ones.