Books

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.

Hypothesis Testing - The Simplest Guide In The Cosmos is a series of 6 short, snappy editable books that will help you reach the right conclusions about your data.These books make no assumptions about your previous experience and are perfect for beginners and those just getting started with hypothesis testing.

Google engineers are regular expression masters. Do you want to become one, too? The Smartest Way to Learn Python Regex transforms you into a regular expression master. The book leverages an innovative learning approach: (1) read a chapter, (2) watch a course video, and (3) solve a code puzzle. It's fun!

For a century, academic dogma claimed that a tiny slice of reality is the exact same size as the whole thing. It’s an illusion. Geometry proves the part is always smaller than the whole. This 30-page blueprint exposes the intellectual traps of Cantorian math and teases a neuroscience layout to tweak your biological waveguide filters. Read the truth, escape the distractions, and join the vanguard.

Large language models can write code, solve problems and reason about the world, but how do they actually work? The Ghost in the Weights takes you inside the black box, exploring the tools and techniques researchers use to uncover the algorithms hidden in neural networks. Learn to trace circuits, decode features and turn opaque computations into something you can understand, test and investigate yourself.

Discover how artificial intelligence is transforming scientific research, from hypothesis generation to autonomous experimentation. This book explores the models, algorithms and computational tools driving a new era of discovery, offering researchers and engineers the knowledge to understand, build and apply AI-powered scientific systems.

A research monograph connecting quantum annealing, Aizenberg MVN theory and complex spectral surrogate models, with detailed applications to battery energy storage placement in power grids.

A rigorous framework for leveraged prediction markets: payoff geometry, microstructure, finality, credit, and protocol design, with empirical studies of Polymarket and Kalshi.

Learn differential calculus through dual numbers and algebraic methods. An illustrated 188-page book covering derivatives, gradients, Hessians, and jets.