Books

A clear, illustrated guide to large language models, covering key concepts and practical applications. Ideal for projects, interviews, or personal learning.

Revised for PyTorch 2.x! In 2019, I published a PyTorch tutorial on Towards Data Science and I was amazed by the reaction from the readers! Their feedback motivated me to write this book to help beginners start their journey into Deep Learning and PyTorch. I hope you enjoy reading this book as much as I enjoy writing it.

Master language models through mathematics, illustrations, and code―and build your own from scratch!

Понятный иллюстрированный путеводитель по ключевым концепциям и практическим применениям больших языковых моделей. Идеально подходит для работы над проектами, подготовки к собеседованиям и самостоятельного обучения.

It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.

A practical guide to fine-tuning Large Language Models (LLMs), offering both a high-level overview and detailed instructions on how to train these models for specific tasks.Get the paperback version here. Get the Kindle version here.

Turn data into decisions. Data Visualization Using Tableau for Data Scientists shows how interactive visuals, analytics, and storytelling come together to make complex data understandable, actionable, and impactful.

大規模言語モデル (LLM) の主要な概念から実践的な応用例まで、簡潔に図解されている学習ガイドです。プロジェクトでの利用、面接対策、個人的な学習にも最適です。

What You Will LearnBy the end of this book, you will be able to:1.     Understand the core concepts of unsupervised learning and how it differs from supervised learning.2.     Preprocess and prepare datasets for clustering, including scaling, normalization, and handling outliers.3.     Implement popular clustering algorithms in Python, tuning parameters for optimal results.4.     Evaluate clustering performance using both internal and external metrics.5.     Apply clustering techniques to real-world problems such as customer segmentation, anomaly detection, and image grouping.6.     Work with high-dimensional data and understand techniques to reduce dimensionality while preserving patterns.7.     Use advanced clustering techniques to solve complex data grouping problems in large datasets.8.     Develop ethical awareness of privacy, bias, and fairness in AI applications. Benefits After Studying This Book For Students ·        Gain strong theoretical foundations in machine learning without supervision.·        Prepare for academic exams, assignments, and competitive exams like UGC NET, GATE, and data science interviews.·        Build portfolio-worthy projects to showcase in internships or job applications. For Job Seekers and Professionals ·        Learn industry-relevant clustering algorithms used in AI, marketing, healthcare, and cybersecurity.·        Enhance data analysis and problem-solving skills to stand out in interviews for roles such as Data Scientist, Machine Learning Engineer, or Business Analyst.·        Understand how to integrate clustering techniques into business solutions for better decision-making. For Researchers and Innovators ·        Explore cutting-edge clustering methods and hybrid models for high-dimensional and big data scenarios.·        Gain insights into current trends and future research opportunities in unsupervised learning.·        Leverage clustering techniques for research publications, AI prototypes, and academic projects.

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.

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.

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.

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.