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Gesamtkatalog

Catalogue complet

Burkov A: Language Models kompakt

Language Models kompakt

Praxisorientierte Sprachmodellierung mit PyTorch

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: <ul> <li>A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes</li> <li>Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML</li> <li>Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis</li> </ul> Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.

Tiny Machine Learning: Design Principles and Applications

Enhancing Reflective Teaching in Higher Education

Redefining Teaching and Learning

GenAI in Higher Education

Embedded System and GUI Development using MATLAB for Engineering Applications

Welche Handlungsspielräume haben wir in einer Welt, die zunehmend von Künstlicher Intelligenz geprägt wird? Was kommt auf uns zu? Dieses Buch lädt dazu ein, unsere Zukunft in einer von KI geprägten Welt bewusst zu reflektieren. Arbeitsplatzverlust, Diskriminierung, Manipulation, Cyberangriffe und das endgültige Verschwinden von Privatsphäre sind nur der Anfang, denn KI wird unser Mensch-Sein an sich in Frage stellen.
Eine unregulierte KI wird unsere Wahrnehmung immer tiefer in virtuelle Metaversen entführen, aus denen es kein Zurück mehr gibt. Sie verändert nicht nur unsere mentalen Lebenswelten, sondern auch unsere Physis. Wenn Menschen durch Body-Enhancement KI-gesteuerte Zusatzfunktionen in ihre Körper integrieren und Roboter mit biologischen Komponenten ausgerüstet werden, verschwimmen die Grenzen zwischen Mensch und Maschine. Wir werden zu Cyborgs – aus Science-Fiction wird eine vorhersehbare Realität.
Um diese Entwicklungen wissenschaftlich, gesellschaftspolitisch und ethisch einzuordnen, begeben wir uns auf eine kulturhistorische Reise, die von der Antike bis in ein vielleicht gar nicht mehr so fernes Morgen führt.

Eine kulturhistorische Reise von der Vergangenheit in unsere Zukunft

Künstliche Intelligenz und Mensch

Advances in Nonlinear Dynamical Systems and Robotics (ANDC)

Swarm Robotics Solutions for National Security and Marine Forensics explores the cutting-edge applications of swarm robotics in safeguarding national security and advancing marine forensics. This book delves into the innovative use of collaborative robotic systems inspired by nature, where multiple autonomous agents work together seamlessly to address complex challenges. From enhancing surveillance and reconnaissance capabilities for national defence to revolutionizing marine forensic investigations, this comprehensive guide highlights the transformative impact of swarm robotics on critical domains. A must-read for researchers, practitioners, and enthusiasts in robotics, artificial intelligence, and security, this book illuminates the future possibilities of swarm robotics in shaping a safer and more secure world.

Swarm Robotics Solutions for National Security and Marine Forensics

Smart Charging Infrastructures

Deep Learning in Computer Vision

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

Augmented and Virtual Reality in Healthcare

Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide provides engineers and system designers who are exploring the application of AI/ML methods for safety-critical systems with a dedicated resource capturing the challenges and mitigation strategies involved in designing such systems. Divided into nine sections, the book covers the most important applications of safety-critical systems, helping readers understand how related problems are being solved in different domains/problem settings. The goal of this book is to help ensure that AI-based critical systems better utilize resources, avoid failures, and increase system safety and public safety. The authors present ML techniques in safety-critical systems across multiple domains, including pattern recognition, image processing, edge computing, Internet of Things (IoT), encryption, hardware accelerators, and many others. These applications help readers understand the many challenges that need to be addressed in order to increase the deployment of ML models in critical systems. In addition, the book shows how to improve public trust in ML systems by providing explainable model outputs rather than treating the system as a black box for which the outputs are difficult to explain. Finally, the authors demonstrate how to meet legal certification and regulatory requirements for the appropriate ML models.

A Comprehensive Guide

Artificial Intelligence and Machine Learning for Safety-Critical Systems

Artificial Intelligence and Machine Learning in Data Science and Analytics

Language Models kompakt

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