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Gesamtkatalog

Catalogue complet

Adaptive AI in Sensor Informatics

Methods, Applications, and Implications

Data-Driven Modeling

Smart Charging Infrastructures

A comprehensive, up-to-date introduction to active inference and the free energy principle for an engineering-focused audience. Active inference, which uses machine learning to model brain function and behavior, emerged from decades of cross-disciplinary research in computational neuroscience, resulting in a vast literature but no unifying treatment. Filling this gap, Sanjeev Namjoshi provides comprehensive coverage of the foundational material needed to understand and navigate this fast-moving field from first principles. Using a simple, conversational style free of proofs, lemmas, and theorems, Namjoshi brings together theory and technical material in one self-contained text. The book begins with an explanation of the general statistical framework used in active inference models that describes the relationship between artificial agents and their environments. It then introduces fundamental concepts in machine learning and statistics and connects them to the active inference perspective. Featuring worked examples, simulations, and detailed walkthroughs of concepts, this user-friendly text aims to expand the readership of active inference to an engineering-focused audience.&nbsp; <ul><li>Provides a one-stop-shop for understanding the foundations and applications of active inference and the free energy principle </li><li>Makes a complex, interdisciplinary subject accessible to students and professionals beyond the neurosciences</li><li>Covers discrete and continuous state-space formulations of active inference as well as state-of-the-art extensions to base active inference methods</li><li>Features extensive appendices and supplemental resources</li></ul>

Principles, Algorithms, and Applications of the Free Energy Principle for Engineers

Fundamentals of Active Inference

Artificial Intelligence and Machine Learning in Data Science and Analytics

Medical Robots and Devices: New Developments and Advances

AI and Data Science in Medical Research focuses on the integration of AI and data science into medical research, highlighting their impact on drug discovery, medical imaging, diagnostics, and genomic medicine. The book addresses the acceleration of therapeutic compound discovery and optimization of drug development pipelines through AI. The volume also discusses advancements in medical imaging, including early disease detection and neuroimaging. Additionally, it covers the application of AI in genomic medicine, offering insights into personalized treatment strategies. The volume concludes with an examination of AI's role in public health surveillance, particularly in disease detection and epidemiological research.

AI and Data Science in Medical Research

Six Dimensions of Business Dark Data

Agentic Hyper-Personalized Dimensions

Medical and Technical Analysis and Signal Processing Perspectives

Exploring Infant Cry

This book is a comprehensive guide that immerses you into the world of building and deploying AI-powered solutions. You are introduced to the core architecture of LLMs and equipped with essential Best Data Practices (BDPs) for utilizing Generative AI responsibly, ensuring ethical and efficient AI deployment.The book starts with the foundational aspects of Generative AI application development. You will learn the nuances of data handling in Generative AI apps, from working with embeddings to managing unstructured and structured data, and handling Personally Identifiable Information (PII) data. The exploration extends to understanding the differences between deterministic and LLM-based data synthesis and integrating Generative AI apps with enterprise data, providing you with practical insights into leveraging data effectively for intelligent applications. A chapter on prompt engineering explains the importance of prompts in AI interactions, covering a spectrum of techniques and pitfalls while offering exercises to enhance prompt engineering skills. As you progress through the book, you take a journey from conceptualization to production and deployment of Generative AI applications. You discover the essentials of Generative AI application development, gain insights into the pathway from ideation to production, and explore the intricacies of LLM selection and fine-tuning.The book equips you with the knowledge and tools necessary to navigate the complex terrain of AI development and deployment, making it an indispensable resource for AI enthusiasts, developers, and business leaders alike.What You Will Learn<ul><li>Know the core architecture of LLMs and how these models have revolutionized AI applications</li><li>Handle various data types, including unstructured, structured, and personally identifiable information (PII) data in Generative AI applications</li><li>Understand prompt engineering and its importance in AI interactions and applications</li><li>Understand modular design of Generative AI apps, essential backend and frontend components, and the unique principles guiding Generative AI app design</li></ul>&nbsp;&nbsp;

This book is a comprehensive guide that immerses you into the world of building and deploying AI-powered solutions. You are introduced to the core architecture of LLMs and equipped with essential Best Data Practices (BDPs) for utilizing Generative AI responsibly, ensuring ethical and efficient AI deployment.The book starts with the foundational aspects of Gehatnerative AI application development. You will learn the nuances of data handling in Generative AI apps, from working with embeddings to managing unstructured and structured data, and handling Personally Identifiable Information (PII) data. The exploration extends to understanding the differences between deterministic and LLM-based data synthesis and integrating Generative AI apps with enterprise data, providing you with practical insights into leveraging data effectively for intelligent applications. A chapter on prompt engineering explains the importance of prompts in AI interactions, covering a spectrum of techniques and pitfalls while offering exercises to enhance prompt engineering skills. As you progress through the book, you take a journey from conceptualization to production and deployment of Generative AI applications. You discover the essentials of Generative AI application development, gain insights into the pathway from ideation to production, and explore the intricacies of LLM selection and fine-tuning.The book equips you with the knowledge and tools necessary to navigate the complex terrain of AI development and deployment, making it an indispensable resource for AI enthusiasts, developers, and business leaders alike.&nbsp;What You Will Learn<ul><li>Know the core architecture of LLMs and how these models have revolutionized AI applications</li><li>Handle various data types, including unstructured, structured, and personally identifiable information (PII) data in Generative AI applications</li><li>Understand prompt engineering and its importance in AI interactions and applications</li><li>Understand modular design of Generative AI apps, essential backend and frontend components, and the unique principles guiding Generative AI app design</li></ul>&nbsp;Who This Book Is ForFrom AI enthusiasts exploring the field to software developers seeking practical insights, and business leaders looking to harness the power of AI for organizational growth and innovation&nbsp;&nbsp;

Adaptive AI in Sensor Informatics

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