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.

Ship a GitHub issue-triage agent from laptop PoC to production - one chapter at a time, fixing real failure modes with contracts, evals, and rollback plans. Built on Pydantic AI for engineers who already know how to ship software and need agents that behave the same way.

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.

Think The Phoenix Project or The Goal, but for the age of AI agents: a business novel that teaches a hard technical subject through story and humor, aimed at the people who have to make decisions about it, as well as people who are actively engaged in architecture and engineering.

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.

As smart factories evolve and artificial intelligence takes center stage, many organizations are asking: Do we still need leaders walking the shop floor? This book answers with a resounding yes.

Part of Lean Foundations & Advanced AI Applications SeriesWhether you're in healthcare, government, education, or manufacturing, AI-Powered Lean helps you reimagine how value is created—and how waste is eliminated—with the aid of intelligent tools.

What's inside:33 ranked optimization patterns from foundational table design through advanced governance. A complete partition and clustering playbook with six Tier 1 patterns.Shuffle reduction techniques built around five patterns that eliminate the most expensive operations. The On-Demand vs Reservations decision framework with diagnostic SQL.A full Guardrails Playbook 

AWS replaced Amazon Q with Kiro — an AI IDE that writes specs before code. 14 chapters covering spec-driven development, AI agents, hooks, MCP integrations, AWS deployment, and the honest comparison with Cursor and Copilot. The definitive guide.

The tech job market changed. Entry-level dropped 73%, AI screens resumes, interviews test system design. This is the no-BS guide: portfolio, resume, LinkedIn, interviews, negotiation, and your first 90 days. Written by someone who hires engineers.

Artificial Intelligence is transforming the world—but it is also creating an entirely new attack surface.What happens when attackers manipulate AI through language itself?Discover how cybercriminals exploit prompt injections, jailbreak models, steal sensitive information, and bypass AI guardrails. Through real-world examples, hands-on labs, secure coding techniques, and enterprise-ready architectures, Mastering LLM Security equips you with the knowledge needed to defend the next generation of intelligent systems.Whether you are building AI products, securing enterprise deployments, or leading cybersecurity programs, this book provides the blueprint for defending modern AI infrastructures.