Large language models

Discover how Category Theory is becoming the mathematical language of modern Artificial Intelligence. Explore categories, functors, natural transformations, compositional learning, neural networks, probabilistic models, and functorial machine learning through an AI-first approach designed for students, researchers, and AI professionals.

Most companies bolt AI onto old workflows. This book shows how to rebuild the company around it: orchestrating AI agents to run real operations, with the governance and cost control to keep them accountable. Written for founders, operators, and leaders who want systems they own, not another subscription.

MCP is the protocol powering the next generation of AI agents, and this is the only book that teaches you all of it. From Python fundamentals to low-level SSE transport, go from zero to production-ready MCP developer.

Your AI prototype works. Now ship it. Most AI frameworks are built for exploration. kdeps is built for production. Define your agent in YAML, declare its dependencies, and deploy it anywhere — Docker, Kubernetes, a standalone binary, an edge device — without rewriting a line when you switch LLM providers. AI Appliances is the hands-on guide to building autonomous AI agents and multi-agent systems with kdeps: deterministic pipelines, real error handling, real deployment, and no vendor lock-in. Write YAML. Run anywhere. Own everything.

"Stop hoping your LLM is secure. Start proving it.The complete framework for scanning, validating, and deploying external models to Vertex AI—using Garak, Rebuff, and LMQL to turn raw base models into trusted, production-ready assets."

Learn to build multi-agent AI workflows that automate your job. Master LangChain, CrewAI, and Claude to create production-grade agent crews for coding, content, customer support, and sales automation.

In addition, the book includes practical frameworks such as the 50-30-20 budgeting rule enhanced with AI, monthly financial review systems, and habit-building strategies that promote long-term financial stability. Real-life case studies are included to demonstrate how different individuals—students, families, freelancers, and small business owners—can apply these concepts in their daily lives.The final sections of the book focus on future trends in AI-driven finance, including hyper-personalized financial systems, integration with blockchain technologies, and the evolving role of human financial advisors. Readers are encouraged to adopt a forward-thinking mindset and prepare for the next generation of financial management.This book is strictly educational in nature and adheres to all relevant publishing and content guidelines. It does not promise guaranteed financial success, quick wealth, or unrealistic outcomes. Instead, it provides knowledge, tools, and frameworks that empower readers to make informed financial decisions.By the end of this book, readers will gain:A clear understanding of personal finance fundamentalsPractical experience with AI-powered financial toolsConfidence in managing money without complex calculationsStructured frameworks for budgeting, saving, and investingAwareness of risks, limitations, and responsible financial behaviorAI-Powered Personal Finance 2026 is not just a book—it is a practical guide designed to help you take control of your financial future in a smart, simple, and sustainable way.

Bestselling book on building LLMs. A practical, project-driven manual for engineers who want to understand how modern language models are built — and where they fail — by writing every layer themselves. From a scalar autograd engine to RLHF to fused specialists, in 36 hands-on projects with deliberate sabotage experiments. Build it. Break it. Measure it.

Detailed Overview of the Book’s ChaptersBelow is a narrative walkthrough of the chapters, showing how each builds upon the previous ones to form a complete, advanced-level textbook. Chapter 1: Advanced Deep Learning Paradigms This chapter introduces paradigms that extend traditional feedforward and convolutional networks. It covers Capsule Networks (CapsNet), which model hierarchical relationships in images better than CNNs, and Neural Ordinary Differential Equations (Neural ODEs), which bring the power of continuous mathematics into deep learning. Readers will also learn about Graph Neural Networks (GNNs) for relational data, Hypernetworks that generate weights for other networks, and Neural Turing Machines (NTMs) that combine computation with memory.Importance: Provides readers with a toolbox of new architectures that go beyond the limits of CNNs and RNNs. Chapter 2: Hybrid and Ensemble Neural Architectures Modern AI is rarely a single architecture—it is often a hybrid system. This chapter explains how Ensemble Learning improves accuracy and robustness, how Neuro-Symbolic AI combines logic with deep learning, and how Mixture of Experts (MoE) models power large-scale language systems like Google’s Switch Transformer. Hybrid CNN-RNN-Attention architectures are also explained with real-world examples in speech and video processing.Importance: Teaches how combining models enhances performance and robustness, preparing readers for cutting-edge AI system design. Chapter 3: Advanced Optimization and Training Strategies Training deep networks is a science in itself. This chapter covers second-order optimization methods, meta-learning, continual learning, and curriculum learning. It also introduces Neural Architecture Search (NAS), which automates the design of optimal architectures.Importance: Equips students with modern training techniques needed to train extremely deep or complex networks efficiently. Chapter 4: Neural Networks for Structured and Non-Euclidean Data Many real-world problems deal with non-Euclidean data such as graphs, networks, and manifolds. This chapter explains Graph Convolutional Networks, Graph Attention Networks, and Spatio-Temporal Networks used in social networks, protein modeling, and traffic prediction.Importance: Prepares readers for the graph revolution in AI, a rapidly growing area in research and applications. Chapter 5: Neural Networks in Reinforcement Learning This chapter integrates deep learning with reinforcement learning to explain how systems like AlphaGo and autonomous vehicles are trained. It covers DQN, Policy Gradient methods, Actor-Critic models, and Multi-Agent RL.Importance: Provides the foundation for building AI systems that learn by interacting with environments, essential for robotics, games, and adaptive decision-making. Chapter 6: Advanced Generative Neural Architectures Going beyond GANs and VAEs, this chapter covers StyleGAN, Diffusion Models, Flow-based Models, and Energy-based Models. It explains how these architectures power text-to-image models, generative art, and scientific simulations.Importance: Essential for students and professionals exploring generative AI, one of the most disruptive areas today. Chapter 7: Neural Networks for Multimodal Learning This chapter explores fusion of multiple data modalities—text, vision, speech—into unified models. It introduces CLIP, Flamingo, and multimodal transformers. Applications in healthcare, AR/VR, and robotics are presented.Importance: Trains readers in building AI that integrates multiple senses, moving toward more general intelligence. Chapter 8: Quantum-Inspired and Neuromorphic Neural Networks This futuristic chapter introduces Quantum Neural Networks (QNNs), Spiking Neural Networks (SNNs), and neuromorphic hardware. It also explores memristors and analog neural computing.Importance: Prepares students for the next paradigm of AI hardware and computation, beyond GPUs and TPUs. Chapter 9: Neural Networks for Real-World Applications This chapter presents detailed applications across healthcare, finance, climate modeling, cybersecurity, and IoT. Each section shows how advanced architectures are applied to practical challenges.Importance: Bridges the gap between theory and practice, showing the impact of neural networks on society. Chapter 10: Research Frontiers in Neural Networks The final chapter summarizes Large Language Models, Scaling Laws, Trustworthy AI, Green AI, and AGI pathways. It invites readers to think critically about what comes next in AI research.Importance: Inspires advanced learners and researchers to contribute to next-generation breakthroughs in neural networks. Why This Book is Essential for Study1.     For Students:o   Provides a clear, structured, and advanced-level curriculum beyond basics.o   Helps in M.Tech, PhD, and UGC NET/AI competitive exams.o   Equips students with knowledge of cutting-edge research areas.2.     For Researchers:o   Serves as a consolidated reference for diverse advanced architectures.o   Saves time by integrating material from scattered research papers.o   Offers insights into emerging frontiers like neuromorphic and quantum AI.3.     For Industry Professionals:o   Enables professionals to adopt latest AI methods in real-world projects.o   Covers practical applications across industries.o   Provides knowledge on multimodal and generative AI, essential in today’s AI-driven world.4.     For Educators:o   Acts as a teaching resource for advanced AI courses.o   Includes examples, applications, and research trends useful for course design. ConclusionThis book is not just another deep learning textbook—it is a gateway to the future of AI. It connects foundational knowledge with cutting-edge innovations, making it indispensable for students, educators, researchers, and professionals alike. By studying this book, readers will be prepared not only to understand today’s most powerful neural architectures but also to contribute to the AI breakthroughs of tomorrow.

Pedagogical Philosophy of the BookThis book is designed with three guiding principles:1.     Clarity over Formalism While maintaining mathematical accuracy, the book avoids unnecessary formalism that can confuse beginners. Instead, it uses intuitive explanations, diagrams, and real-world analogies.2.     Integration of Computation Every mathematical concept is tied to computational practice. Readers are encouraged to implement simple code snippets (in Python, NumPy, or similar tools) to reinforce their understanding.3.     Balance Between Breadth and Depth The book covers the essential calculus concepts in sufficient depth to support AI applications, without delving into overly abstract branches that have limited relevance to machine learning. Who Should Read This Book?·        Students of Computer Science, Data Science, and AI – who want to strengthen their mathematical foundation for advanced courses and projects.·        Researchers in AI – who need a refresher or structured guide to connect calculus with modern algorithms.·        Industry Professionals and Engineers – who want to move beyond using libraries like TensorFlow or PyTorch blindly and instead gain an understanding of the mathematics behind the models.·        Educators – who seek a resource that connects abstract mathematics with practical AI examples for teaching purposes.Benefits of Studying This Book1.     Builds Mathematical Confidence – Readers who once found calculus intimidating will discover a fresh, accessible perspective tailored for AI.2.     Enables Deeper Understanding of Algorithms – Going beyond “black box” usage of AI tools, readers will understand why models work.3.     Enhances Problem-Solving Skills – By mastering calculus-driven optimization, readers can design new models and improve existing ones.4.     Supports Academic and Career Growth – Mastery of calculus strengthens research capabilities, technical interviews, and advanced study opportunities.5.     Encourages Critical Thinking – Rather than rote memorization, the book fosters curiosity about the connections between mathematics and intelligent systems. The Long-Term VisionArtificial Intelligence is not just a passing trend—it is shaping the future of science, technology, and human society. Calculus, as a timeless branch of mathematics, ensures that learners have the intellectual tools to adapt to new paradigms. As AI expands into quantum computing, neuroscience-inspired architectures, and beyond, the reliance on calculus will remain unshaken.This book provides readers not just with knowledge, but with intellectual independence—the ability to reason about algorithms, derive insights, and innovate confidently.   

5. How This Book is Useful for StudyThis book is carefully designed to meet the needs of students, teachers, and researchers in computer science, artificial intelligence, and applied mathematics. Here’s how it will benefit learners:1.     Conceptual Clarity: The book explains abstract concepts like fuzzy sets, fuzzy logic, and fuzzy inference in simple and student-friendly language.2.     Step-by-Step Approach: Mathematical derivations, examples, and solved problems are provided in a logical sequence.3.     Exam-Oriented Content: The text covers topics that are important for BCA, MCA, B.Tech, M.Tech, and UGC NET examinations.4.     Practical Relevance: Numerous case studies and applications in AI systems are provided, making the subject practical and engaging.5.     Interdisciplinary Approach: The book connects mathematics, computer science, and engineering, preparing students for multidisciplinary research.6.     Hands-On Learning: Exercises, numerical problems, and small projects are suggested for practice.7.     Self-Study Friendly: Designed in such a way that even students without a strong background in higher mathematics can grasp the concepts.In summary, this book transforms fuzzy mathematics from a theoretical subject into a practical skill set essential for AI study and research. 6. Why This Book is Different and ImportantWhile there are many books on fuzzy logic or AI separately, very few combine mathematical foundations with AI applications in such a comprehensive, student-oriented way.·        Balanced Coverage: Unlike purely theoretical texts, this book strikes a balance between mathematical rigor and practical applications.·        Accessible Style: Concepts are explained in simple terms, supported with examples, diagrams, and illustrations.·        Focus on Students: Written with examination and career needs in mind, making it directly useful for academic success.·        AI-Oriented Approach: Unlike classical fuzzy mathematics books, this one emphasizes how fuzzy tools integrate into AI systems.·        Future-Ready Content: Emerging areas like fuzzy deep learning, quantum fuzzy logic, and hybrid systems are included to prepare students for future research.This makes the book not just a textbook, but a career-building guide for students of AI. 7. Pedagogical FeaturesTo make learning engaging and effective, the book is structured with special features such as:·        Definitions and Key Points highlighted for quick revision.·        Illustrations and Diagrams for visual understanding.·        Solved Examples to demonstrate problem-solving techniques.·        Exercises and Practice Problems at the end of each chapter.·        Case Studies to connect theory with real-life AI applications.·        Summary Sections for quick review before exams. 8. Who Should Read This Book? (Target Audience)This book is intended for a wide range of learners:·        Undergraduate Students (BCA, B.Sc., B.Tech): To build a foundation in fuzzy mathematics and AI.·        Postgraduate Students (MCA, M.Sc., M.Tech): For advanced study and research in AI and soft computing.·        Competitive Exam Aspirants (UGC NET, GATE): As a reference guide for key topics in AI and fuzzy logic.·        Researchers and Scholars: To explore applications of fuzzy systems in machine learning, NLP, and robotics.·        Industry Professionals: To understand the practical applications of fuzzy logic in AI-based projects. 9. Practical Outcomes After Studying the BookBy the end of this book, readers will be able to:·        Understand the mathematical foundations of fuzzy sets, relations, and logic.·        Apply fuzzy reasoning to solve uncertain and imprecise problems.·        Design fuzzy inference systems and controllers for AI applications.·        Implement fuzzy clustering and decision-making models in machine learning.·        Integrate fuzzy systems with neural networks and genetic algorithms.·        Contribute to research in advanced areas like fuzzy deep learning and quantum fuzzy logic.This ensures that learners not only gain theoretical knowledge but also practical competence in applying fuzzy mathematics in AI.

How to Use This BookThis book is structured to guide you through the complexities of neuromorphic computing in a logical and progressive manner:Part I: Foundations of Neuromorphic Computing lays the groundwork by introducing the core concepts, historical context, and the biological inspirations behind neuromorphic architectures. If you're new to the subject, start here to build a strong conceptual understanding.Part II: Hardware Architectures and Technologies dives deep into the silicon, exploring the leading neuromorphic chips and the emerging technologies that are making these brain-inspired systems a reality. This section is crucial for understanding the physical manifestation of neuromorphic principles.Part III: Algorithms and Learning Mechanisms focuses on the software side, detailing the learning rules and programming frameworks that enable neuromorphic systems to learn and adapt. For those looking to work with or develop for these systems, this part offers essential practical insights.Part IV: Applications and Case Studies showcases the diverse and impactful uses of neuromorphic computing across various domains, from computer vision to robotics and cognitive modeling. These chapters provide real-world context and inspiration.Part V: Future Directions and Research Frontiers looks ahead, addressing the challenges, ethical considerations, and the exciting future possibilities of neuromorphic computing in a post-Moore's Law era.Feel free to read the book cover-to-cover for a comprehensive understanding, or jump to specific chapters based on your interests. Each chapter is designed to be largely self-contained while building upon previous concepts.

How to Use This BookThis book is structured to guide you through the complexities of neuromorphic computing in a logical and progressive manner:Part I: Foundations of Neuromorphic Computing lays the groundwork by introducing the core concepts, historical context, and the biological inspirations behind neuromorphic architectures. If you're new to the subject, start here to build a strong conceptual understanding.Part II: Hardware Architectures and Technologies dives deep into the silicon, exploring the leading neuromorphic chips and the emerging technologies that are making these brain-inspired systems a reality. This section is crucial for understanding the physical manifestation of neuromorphic principles.Part III: Algorithms and Learning Mechanisms focuses on the software side, detailing the learning rules and programming frameworks that enable neuromorphic systems to learn and adapt. For those looking to work with or develop for these systems, this part offers essential practical insights.Part IV: Applications and Case Studies showcases the diverse and impactful uses of neuromorphic computing across various domains, from computer vision to robotics and cognitive modeling. These chapters provide real-world context and inspiration.Part V: Future Directions and Research Frontiers looks ahead, addressing the challenges, ethical considerations, and the exciting future possibilities of neuromorphic computing in a post-Moore's Law era.Feel free to read the book cover-to-cover for a comprehensive understanding, or jump to specific chapters based on your interests. Each chapter is designed to be largely self-contained while building upon previous concepts.

Target AudienceThis book is designed for a diverse audience, including:Students and Academics: Individuals pursuing studies in computer science, physics, engineering, and related fields, seeking to expand their knowledge into the realms of Quantum Computing and AI.Industry Professionals: Engineers, data scientists, and AI practitioners interested in understanding how quantum technologies can enhance their work and open new avenues for innovation.Researchers and Innovators: Those engaged in cutting-edge research or entrepreneurial ventures aiming to explore the intersection of quantum technologies and artificial intelligence.Policy Makers and Thought Leaders: Individuals involved in shaping the future of technology policy, ethics, and regulation, seeking insights into the implications of Quantum AI.  Global RelevanceThe integration of Quantum Computing and AI is not confined to theoretical research but is actively influencing technological development worldwide. Companies like Google, IBM, and IonQ are leading the charge in quantum research, while AI advancements continue to permeate various industries. The global nature of these developments underscores the importance of understanding and engaging with these technologies, regardless of geographical location.  Benefits of Studying This BookStudying Quantum AI & Beyond offers numerous advantages:Comprehensive Knowledge: Gain a thorough understanding of both Quantum Computing and AI, and how their convergence is shaping the future of computing.Practical Insights: Learn about real-world applications and case studies that demonstrate the transformative potential of Quantum AI.Ethical Awareness: Develop an understanding of the ethical considerations and societal impacts associated with these technologies.Future Preparedness: Equip yourself with the knowledge to anticipate and adapt to the evolving technological landscape.Career Advancement: Enhance your expertise in a cutting-edge field, positioning yourself at the forefront of technological innovation.   

Global RelevanceThe integration of Quantum Computing and AI is not confined to theoretical research but is actively influencing technological development worldwide. Companies like Google, IBM, and IonQ are leading the charge in quantum research, while AI advancements continue to permeate various industries. The global nature of these developments underscores the importance of understanding and engaging with these technologies, regardless of geographical location.  Benefits of Studying This BookStudying Quantum AI & Beyond offers numerous advantages:Comprehensive Knowledge: Gain a thorough understanding of both Quantum Computing and AI, and how their convergence is shaping the future of computing.Practical Insights: Learn about real-world applications and case studies that demonstrate the transformative potential of Quantum AI.Ethical Awareness: Develop an understanding of the ethical considerations and societal impacts associated with these technologies.Future Preparedness: Equip yourself with the knowledge to anticipate and adapt to the evolving technological landscape.Career Advancement: Enhance your expertise in a cutting-edge field, positioning yourself at the forefront of technological innovation.