Books

Can Hidden Markov Models predict financial market regimes?How are genes discovered from DNA sequences?How do robots determine their location in uncertain environments?Can probabilistic AI still compete with Transformers and Deep Learning?Volume-3 of Hidden Markov Models and AI answers these questions through real-world applications, Python implementations, industrial case studies, advanced projects, and future AI research.Explore how Hidden Markov Models are used in bioinformatics, cybersecurity, finance, robotics, autonomous systems, anomaly detection, and scientific discovery. Learn to build HMM systems from scratch, work with professional AI libraries, and understand the evolving relationship between probabilistic models and deep learning.This volume is designed for readers who want to move beyond theory and develop practical expertise in modern sequential artificial intelligence.

How do voice assistants understand speech?How does a chatbot track conversation context?How can machines identify speakers, translate languages, recognize named entities, and process sequential information?The answer lies in sequence modeling.In Volume-2 of Hidden Markov Models and AI, readers move beyond theory into practical applications of Hidden Markov Models in speech recognition, natural language processing, machine translation, speaker verification, conversational AI, and intelligent decision-making systems.Learn how modern AI systems transform speech signals and language sequences into meaningful intelligence using probabilistic models that continue to influence today's most advanced technologies.Whether you are an AI student, NLP researcher, speech engineer, or machine learning professional, this volume provides the practical knowledge required to master sequential learning systems.

Artificial Intelligence is not only about neural networks and transformers. Behind many of the world's most influential AI systems lies a powerful probabilistic framework known as the Hidden Markov Model (HMM).From speech recognition and natural language processing to robotics, cybersecurity, finance, and bioinformatics, HMMs remain one of the most important sequence modeling techniques ever developed.This book takes readers on a complete journey through Markov Chains, probabilistic reasoning, Hidden Markov Models, Forward-Backward algorithms, Viterbi decoding, Baum-Welch training, and advanced HMM architectures.Designed for students, researchers, and AI professionals, the book combines rigorous mathematics with practical applications, making complex concepts accessible and immediately useful.If you want to truly understand how intelligent systems model uncertainty, learn from temporal patterns, and reason about hidden information, this book provides the foundation.

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.

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.

Are you ready for Legislative Power 5.0? Artificial Intelligence is a powerful tool and ally in the art of Lawmaking. Technology has changed the rules of the game. This book teachs you how to master them.

Systems fail in ways that existing frameworks cannot see. Not because the knowledge was absent — because it failed to function. Eleven failure modes. One common grammar. A field guide for practitioners who run complex systems and know something is wrong before they can name it.

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.

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.

We’ve spent years designing screens.The future belongs to designers who can design systems that learn.Designing Adaptive Products explores adaptive UX, personalization, behavioral signals, dynamic interfaces, and the balance between automation and human control, redefining what product design looks like in the AI era.

Your first AI integration is usually simple: one provider, one SDK, one model. Then production happens. Costs rise. Rate limits appear. Teams need access control. Security wants API keys centralized. Product wants to test better models. Finance wants to know who is spending what. Suddenly, the real problem is not calling an LLM. The real problem is operating AI as shared infrastructure. The End of AI Vendor Lock-In shows how LiteLLM helps teams build provider-agnostic, resilient, cost-aware AI systems that can route across models, manage fallbacks, track spend, enforce budgets, and reduce dependency on any single provider. If you are building AI products that need to survive real-world production pressure, this book is for you.