Books

Every intelligent prediction begins with a simple question:Can we model the relationship between data and outcomes?Regression is the foundation upon which modern predictive analytics, machine learning systems, and countless AI applications are built.From linear regression and regularization techniques to neural network regression, Gaussian processes, explainable AI, and real-world predictive systems, this book provides a complete roadmap for understanding how machines learn to predict.In Linear and Nonlinear Regression in Artificial Intelligence, Anshuman Mishra combines mathematical rigor, practical implementation, and real-world applications to help readers master one of the most powerful tools in Artificial Intelligence.Discover the mathematics behind prediction—and the science behind intelligent decision-making.

Artificial Intelligence does not learn in certainty.It learns through uncertainty, exploration, randomness, and adaptation.How does AlphaGo evaluate millions of possible moves?How do reinforcement learning agents discover optimal strategies?How do diffusion models generate realistic images?How do autonomous robots navigate uncertain environments?The answer lies in stochastic processes.In this advanced second volume, Anshuman Mishra explores the mathematical foundations behind reinforcement learning, probabilistic deep learning, robotics, generative AI, and emerging stochastic algorithms that are shaping the future of intelligent systems.Discover how randomness becomes learning—and how uncertainty becomes intelligence.

Artificial Intelligence is often described as learning from data.But beneath every learning algorithm lies something even more fundamental:Probability, randomness, and uncertainty.From Hidden Markov Models and stochastic gradient descent to Monte Carlo methods and reinforcement learning, modern AI systems depend on stochastic processes to make predictions, learn from experience, and adapt to changing environments.In Stochastic Processes in Artificial Intelligence, Anshuman Mishra provides a structured and accessible journey through the mathematical foundations that power intelligent systems.Discover how uncertainty becomes intelligence—and why stochastic thinking is essential for the future of Artificial Intelligence.

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.

What if the unpredictable behavior of intelligent systems is not a flaw—but a feature?In Nonlinear Dynamics and Chaos Theory in Artificial Intelligence (VOL-II), Anshuman Mishra explores the fascinating intersection of chaos, complexity, fractals, adaptive intelligence, and machine learning.Discover how chaotic neural networks, nonlinear optimization, fractal learning architectures, autonomous robotics, reinforcement learning, and emergent intelligence are transforming the future of AI.Through mathematical rigor, practical Python implementations, real-world case studies, and cutting-edge research directions, this book reveals how chaos can become a powerful tool for building smarter, more adaptive, and more resilient intelligent systems.For researchers, students, engineers, and AI innovators, this volume opens the door to one of the most exciting frontiers of next-generation artificial intelligence.

What if the unpredictable behavior of AI systems is not a flaw but a consequence of deeper mathematical laws? Explore chaos theory, nonlinear dynamics, fractals, emergence, and complexity to uncover how intelligent systems learn, adapt, self-organize, and evolve in ways that traditional linear models cannot explain.

Explore the advanced world of AI reasoning through SAT and SMT solvers, knowledge representation, intelligent agents, logic programming, formal verification, and neuro-symbolic AI. Learn how modern intelligent systems reason, prove, verify, and explain decisions with mathematical precision.

Discover the mathematical foundations behind intelligent reasoning. Explore propositional logic, predicate logic, theorem proving, SAT and SMT solvers, knowledge representation, formal verification, and AI reasoning systems in one comprehensive guide designed for students, researchers, and AI professionals.

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.

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.

Serious AI research no longer needs a data center. This is the field log of training, fine-tuning, serving, and shipping real models on a single petascale desktop, with the code to reproduce every result. For engineers building AI on NVIDIA hardware who want depth, not hype.

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.

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."