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Hardcover. Zustand: new. Hardcover. Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of Tiny Machine Learning (TinyML), enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge Internet of Things (IoT) nodes. This book provides a comprehensive guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments.This book offers thorough coverage of key topics, including:Foundations and Optimization of TinyML: Covers microcontroller-centric power optimization, core principles, and algorithms essential for deploying efficient machine learning models on embedded systems with strict resource constraints.Applications of TinyML in Healthcare and IoT: Presents innovative use cases such as compact artificial intelligence (AI) solutions for healthcare challenges, real-time detection systems, and integration with low-power IoT and low-power wide-area network (LPWAN) technologies.Security and Privacy in TinyML: Addresses the unique challenges of securing TinyML deployments, including privacy-preserving techniques, blockchain integration for secure IoT applications, and methods for protecting resource-constrained devices.Emerging Trends and Future Directions: Explores the evolving landscape of TinyML research, highlighting new applications, adaptive frameworks, and promising avenues for future investigation.Practical Implementation and Case Studies: Offers hands-on insights and real-world examples demonstrating TinyML in action across diverse scenarios, providing guidance for engineers, researchers, and students.This book is an essential resource for embedded system designers, AI practitioners, cybersecurity professionals, and academics who want to harness the power of TinyML for smarter, more efficient, and secure edge intelligence solutions. Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of TinyML, enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge IoT nodes. It is a guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Hardcover. Zustand: new. Hardcover. Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of Tiny Machine Learning (TinyML), enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge Internet of Things (IoT) nodes. This book provides a comprehensive guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments.This book offers thorough coverage of key topics, including:Foundations and Optimization of TinyML: Covers microcontroller-centric power optimization, core principles, and algorithms essential for deploying efficient machine learning models on embedded systems with strict resource constraints.Applications of TinyML in Healthcare and IoT: Presents innovative use cases such as compact artificial intelligence (AI) solutions for healthcare challenges, real-time detection systems, and integration with low-power IoT and low-power wide-area network (LPWAN) technologies.Security and Privacy in TinyML: Addresses the unique challenges of securing TinyML deployments, including privacy-preserving techniques, blockchain integration for secure IoT applications, and methods for protecting resource-constrained devices.Emerging Trends and Future Directions: Explores the evolving landscape of TinyML research, highlighting new applications, adaptive frameworks, and promising avenues for future investigation.Practical Implementation and Case Studies: Offers hands-on insights and real-world examples demonstrating TinyML in action across diverse scenarios, providing guidance for engineers, researchers, and students.This book is an essential resource for embedded system designers, AI practitioners, cybersecurity professionals, and academics who want to harness the power of TinyML for smarter, more efficient, and secure edge intelligence solutions. Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of TinyML, enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge IoT nodes. It is a guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Prof. Khalid El Makkaoui is an Associate Professor with the Department of Computer Science at the Multidisciplinary Faculty of Nador, University Mohammed Premier, Oujda, Morocco. His research interests focus on cybersecurity and artificial intelli.

Buch. Zustand: Neu. Tiny Machine Learning Techniques for Constrained Devices | Khalid El-Makkaoui (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2026 | Chapman and Hall/CRC | EAN 9781032897523 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Hardcover. Zustand: new. Hardcover. Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of Tiny Machine Learning (TinyML), enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge Internet of Things (IoT) nodes. This book provides a comprehensive guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments.This book offers thorough coverage of key topics, including:Foundations and Optimization of TinyML: Covers microcontroller-centric power optimization, core principles, and algorithms essential for deploying efficient machine learning models on embedded systems with strict resource constraints.Applications of TinyML in Healthcare and IoT: Presents innovative use cases such as compact artificial intelligence (AI) solutions for healthcare challenges, real-time detection systems, and integration with low-power IoT and low-power wide-area network (LPWAN) technologies.Security and Privacy in TinyML: Addresses the unique challenges of securing TinyML deployments, including privacy-preserving techniques, blockchain integration for secure IoT applications, and methods for protecting resource-constrained devices.Emerging Trends and Future Directions: Explores the evolving landscape of TinyML research, highlighting new applications, adaptive frameworks, and promising avenues for future investigation.Practical Implementation and Case Studies: Offers hands-on insights and real-world examples demonstrating TinyML in action across diverse scenarios, providing guidance for engineers, researchers, and students.This book is an essential resource for embedded system designers, AI practitioners, cybersecurity professionals, and academics who want to harness the power of TinyML for smarter, more efficient, and secure edge intelligence solutions. Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of TinyML, enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge IoT nodes. It is a guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of TinyML, enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge IoT nodes. This book provides a comprehensive guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments.