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Paperback Agentic AI: Designing Autonomous Systems for Next-Generation Intelligence: Mastering Autonomous Agents, Reinforcement Learning, Multi-Agent Systems, D Book

ISBN: B0FQ3SNM5C

ISBN13: 9798264108457

Agentic AI: Designing Autonomous Systems for Next-Generation Intelligence: Mastering Autonomous Agents, Reinforcement Learning, Multi-Agent Systems, D

Agentic AI represents the frontier of artificial intelligence, where systems can independently make decisions, adapt to dynamic environments, and achieve complex goals with minimal human intervention. This book provides a definitive guide to understanding and building these intelligent, autonomous systems, offering both theoretical insights and practical tools for real-world applications.- Who This Book Is For:
This book is crafted for AI developers, machine learning engineers, data scientists, and technology strategists passionate about advancing artificial intelligence through agentic systems. It's ideal for professionals aiming to design autonomous agents capable of independent decision-making, as well as researchers and academics exploring the theoretical and practical implications of AI autonomy. Whether you're a seasoned practitioner or an aspiring innovator in fields like robotics, finance, or healthcare, this book equips you with the tools to excel.
- What's Inside the Book:
Spanning 15 comprehensive chapters, Agentic AI: Designing Autonomous Systems for Next-Generation Intelligence offers an in-depth exploration of agentic AI, from foundational concepts to cutting-edge applications. The book begins with the principles of autonomous agents, covering reinforcement learning, Markov decision processes, and goal-directed behavior. It progresses to advanced topics like multi-agent systems, collaborative and competitive environments, and ethical AI frameworks. Readers will find practical tutorials using Python, TensorFlow, and PyTorch, alongside case studies from industries such as autonomous vehicles, supply chain optimization, and personalized medicine. Each chapter includes actionable code snippets, architectural diagrams, and real-world scenarios, ensuring a balance of theory and practice. The book also addresses challenges like scalability, robustness, and safety in deploying agentic AI systems.
- What You Will Learn:
o Master the design of autonomous agents using reinforcement learning and decision-making algorithms.
o Implement multi-agent systems for collaborative tasks like swarm robotics or market simulations.
o Develop robust AI models that adapt to dynamic, real-world environments.
o Navigate ethical considerations, including bias mitigation and safety protocols for autonomous systems.
o Deploy agentic AI solutions in high-stakes domains, with practical insights into performance optimization and monitoring.

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