Books

Artificial Intelligence learns from information.But how do modern AI systems decide what information to keep and what to discard?Why do GANs generate realistic images?How do large language models compress knowledge?What role does entropy play in reinforcement learning?Can information theory explain intelligence itself?Information Theory and Artificial Intelligence: Entropy, Coding, Regularization, and Generative Models (Volume II) explores the advanced information-theoretic foundations behind today's most powerful AI systems.Inside this volume, you will discover:✓ Contrastive Learning and Representation Learning✓ InfoNCE Loss and Mutual Information Estimation✓ Self-Supervised Learning Architectures✓ Generative Adversarial Networks (GANs)✓ Wasserstein GANs and InfoGAN✓ Mode Collapse and GAN Stability✓ Entropy-Driven Reinforcement Learning✓ Soft Actor-Critic Frameworks✓ Fisher Information and Natural Gradients✓ Information Geometry for Neural Networks✓ Information Theory Behind Large Language Models✓ Token Entropy and Perplexity✓ Transformer Information Routing✓ Federated Learning Communication Constraints✓ Explainable AI Through Entropy and Mutual Information✓ Quantum Information Theory and AI✓ Information Bottleneck Theory✓ AI Fairness, Safety, Alignment, and Future Research ChallengesWhether you are a student, researcher, educator, or AI professional, this volume provides the mathematical and conceptual tools required to understand the information-processing principles that drive modern intelligent systems.Learn how information becomes intelligence.

What is intelligence?At its core, intelligence is the ability to process information.But what exactly is information?How do neural networks compress knowledge?Why does cross-entropy dominate machine learning?How do Variational Autoencoders learn latent representations?What role does entropy play in regularization, generalization, and modern AI systems?Information Theory and Artificial Intelligence: Entropy, Coding, Regularization, and Generative Models (Volume I) provides a comprehensive exploration of the mathematical foundations that power modern artificial intelligence.Inside this volume, you will discover:✓ Shannon Entropy and Measures of Information✓ Mutual Information and Feature Learning✓ Cross-Entropy and Machine Learning Loss Functions✓ Kullback–Leibler (KL) Divergence✓ Source Coding and Data Compression✓ Huffman, Arithmetic, and Lempel–Ziv Coding✓ Channel Capacity and Communication Limits✓ Information-Theoretic Learning Principles✓ Information Bottleneck Theory✓ Entropy-Based Regularization✓ Neural Networks as Information Processing Systems✓ Error-Correcting Codes and AI Robustness✓ Neural Communication Systems✓ Variational Inference and Variational Autoencoders (VAEs)Whether you are a student, researcher, educator, or AI professional, this book provides the conceptual and mathematical foundation necessary to understand how information drives learning, intelligence, and modern AI systems.Learn not just how AI works—but why it works.

Artificial Intelligence is powered by neural networks.But how do neural networks actually learn?How do systems recognize faces, understand speech, generate text, and create images?Neural Networks and Architectures: A Comprehensive Guide for Students provides a complete learning pathway from fundamental concepts to state-of-the-art deep learning architectures.Inside this book, you will discover:✓ Biological inspiration behind neural networks✓ Mathematical foundations of deep learning✓ Perceptrons and Multi-Layer Perceptrons (MLPs)✓ Forward Propagation and Backpropagation✓ Activation Functions and Regularization Techniques✓ Convolutional Neural Networks (CNNs)✓ Recurrent Neural Networks (RNNs), LSTM, and GRU✓ Autoencoders and Generative Adversarial Networks (GANs)✓ Transformer Architecture, BERT, and GPT✓ Optimization Algorithms and Model Training✓ Practical Projects using TensorFlow, PyTorch, and Keras✓ Ethical Considerations and Future Research DirectionsDesigned for students, educators, researchers, and AI enthusiasts, this book combines theory, mathematics, implementation, and applications into one comprehensive learning resource.Build a strong foundation in neural networks and prepare yourself for the future of Artificial Intelligence.

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.

What happens when intelligent machines must compete, cooperate, negotiate, and learn in the same environment?The answer lies at the intersection of Game Theory and Artificial Intelligence.From autonomous vehicles negotiating road traffic to trading algorithms competing in financial markets, from multi-robot coordination to cybersecurity defense systems, modern AI increasingly operates in strategic environments involving multiple decision-makers.This book provides a complete journey through:✓ Classical Game Theory and Nash Equilibrium✓ Multi-Agent Systems and Distributed Intelligence✓ Reinforcement Learning and Multi-Agent RL✓ Deep Reinforcement Learning Algorithms✓ Mechanism Design and Incentive Engineering✓ AI Applications in Robotics, Economics, Networks, and Cybersecurity✓ Evolutionary and Quantum Game Theory✓ AI Safety, Governance, and AlignmentWritten for students, researchers, educators, and professionals, this book bridges mathematical foundations with cutting-edge AI research and real-world applications.Whether you are learning game theory for the first time or exploring advanced multi-agent intelligence, this book offers the knowledge and tools needed to understand the future of strategic artificial intelligence.

"The AI confirmed X for me" used as proof of X. Outputs that sound brilliant but don't hold up to a severe re-reading. A three-page "AI policy" that nobody reads. Sound familiar? Thinking with LLMs, the Right Way is the system of critical thinking applied to LLMs: the Thinking-With Triangle (Intent / Adversary / Editor), the four meta-decisions of governance, the Socratic and adversarial practices for investigating and verifying. Not prompt engineering: the method for not letting yourself be mirrored.

LLMs and agentic AI are currently generating a great deal of hype. When applied correctly, they can deliver tremendous benefits. This book outlines the challenges involved in implementing these technologies within large enterprises—particularly in regulated environments. It serves as an accessible introduction for IT-focused executives and enterprise architects, while also proving useful for IT professionals in general who wish to explore the subject and avoid common project pitfalls.

Artificial Intelligence is no longer just a tool for generating text.Modern AI systems can understand documents, analyze images, process audio, interpret structured data, and assist with complex reasoning tasks. Gemini 3 represents a significant advancement in multimodal AI, enabling students, researchers, and professionals to interact with information in more intelligent and productive ways.This practical guide explores how Gemini 3 works, how multimodal AI systems process different types of information, and how users can integrate these technologies into learning, research, communication, and professional workflows.Inside this book, you will discover:✓ How multimodal AI processes text, images, documents, and audio✓ Effective prompt engineering strategies✓ AI-assisted learning techniques for students✓ Research workflows powered by AI✓ Professional productivity and knowledge management applications✓ Responsible AI usage and risk awareness✓ Step-by-step working models for real-world implementationWhether you are a student preparing for the future, a researcher managing large volumes of information, or a professional seeking greater productivity, this book provides a practical roadmap for leveraging Gemini 3 in an AI-driven world.

Artificial Intelligence is changing how we learn, research, create, and work.But what exactly is GPT-5?How do modern multimodal AI systems understand text, images, documents, and other forms of information simultaneously?How can students, researchers, educators, and professionals use these tools responsibly and effectively?In GPT-5 and Multimodal AI: A Practical Guide for Students, Researchers, and Professionals, author Anshuman Mishra provides a clear, practical, and educational roadmap for understanding the next generation of intelligent systems.From neural networks and transformer architectures to AI-assisted learning, academic research, professional productivity, and responsible innovation, this book explains both the technology and its real-world applications.Readers will discover how AI can support personalized learning, research workflows, software development, business productivity, and creative problem-solving while maintaining the importance of human judgment, ethics, and critical thinking.Whether you are a student preparing for the future, a researcher seeking new tools, an educator exploring innovative teaching methods, or a professional adapting to digital transformation, this book provides the knowledge and practical skills needed to thrive in the age of AI.The future belongs not to those who fear AI, but to those who understand it.

Artificial Intelligence is no longer a distant possibility—it is becoming a collaborative partner in education, research, software development, and professional decision-making.But how do frontier AI systems actually work?What makes safety-centered AI different from earlier generations of language models?How can students, researchers, educators, and professionals prepare for a future shaped by increasingly capable AI systems?In Claude 4 (Anthropic): Safety-First Frontier AI in 2026, author Anshuman Mishra presents a comprehensive and academically grounded exploration of one of the most influential frontier AI systems of the modern era.From Constitutional AI and advanced reasoning architectures to multimodal intelligence, agentic workflows, educational transformation, governance frameworks, workforce implications, and future AI research directions, this book provides readers with a balanced understanding of both opportunities and challenges.Rather than focusing on hype or speculation, the book emphasizes evidence-based analysis, responsible innovation, ethical deployment, and human-centered AI development.Whether you are a student seeking AI literacy, a researcher exploring frontier models, a professional adapting to technological change, or a policymaker shaping future governance frameworks, this book offers the knowledge needed to engage thoughtfully with the next generation of intelligent systems.The future of AI will not be determined by technology alone.It will be shaped by how responsibly humanity chooses to use it.

How does a machine recognize a face?How can AI distinguish speech from noise?Why do modern computer vision systems still rely on mathematical techniques developed decades ago?The answer lies in Fourier and Wavelet Analysis.In Fourier and Wavelet Analysis in Artificial Intelligence, Anshuman Mishra reveals how frequency-domain representations, multi-resolution analysis, and signal-processing techniques continue to shape the future of Machine Learning, Deep Learning, Computer Vision, Speech Recognition, Biomedical AI, and Edge Intelligence.From Fourier Transforms and Fast Fourier Algorithms to Wavelet Scattering Networks and Hybrid CNN Architectures, this book demonstrates how mathematical signal analysis becomes intelligent feature extraction.Discover the mathematics behind perception, representation, and intelligent decision-making.

Can a neural network be viewed as a differential equation?Why does gradient descent behave like a dynamical system?How do biological neurons inspire modern AI architectures?What mathematical principles govern stability, learning, adaptation, and intelligence?In Differential Equations in AI and Neural Dynamics, Anshuman Mishra explores the mathematical framework that underlies modern Artificial Intelligence.From Ordinary Differential Equations and Neural Population Models to Neural ODEs, Stochastic Learning, Reinforcement Learning, and Brain-Inspired Computation, this book reveals how continuous-time mathematics drives intelligent behavior.Discover how equations of change become equations of intelligence.

What do speech recognition systems, computer vision models, autonomous robots, and biomedical AI applications have in common?They all rely on the mathematics of signal transformation.How does a neural network extract meaningful patterns from raw audio?Why are Fourier features becoming increasingly important in machine learning?How can Laplace and Z-Transforms help analyze dynamic systems, sequential data, and intelligent control architectures?In Integral Transforms for Artificial Intelligence, Anshuman Mishra reveals how Fourier, Laplace, and Z-Transform techniques power modern AI systems across machine learning, deep learning, computer vision, speech processing, robotics, and signal analysis.Discover how mathematical transformations convert raw signals into intelligent insights—and how they continue to shape the future of Artificial Intelligence.

Every neural network is a collection of matrix operations.Every convolution is a structured matrix transformation.Every deep learning breakthrough ultimately depends on efficient tensor computations.But how do these mathematical operations actually power intelligent systems?In Matrix Computations for Deep Learning, Anshuman Mishra reveals the mathematical engine behind modern AI. From Singular Value Decomposition and tensor algebra to convolutional neural networks, GPU acceleration, and large-scale machine learning systems, this book provides a complete roadmap for understanding the computational foundations of deep learning.Discover how matrices become intelligence—and how mathematics becomes machine learning.

How should an AI system make decisions when information is incomplete?How can machines quantify uncertainty instead of merely producing predictions?How can intelligent systems continuously update their beliefs as new evidence emerges?The answer lies in Bayesian Mathematics.In Bayesian Mathematics for AI Decision Making, Anshuman Mishra explores the powerful framework that enables modern AI systems to reason probabilistically, model uncertainty, and make rational decisions in complex environments.From Bayesian inference and probabilistic programming to uncertainty-aware deep learning, reinforcement learning, healthcare diagnostics, robotics, and financial forecasting, this book reveals how Bayesian thinking is shaping the next generation of Artificial Intelligence.Learn how uncertainty becomes knowledge—and how probability becomes intelligence.