9781032897523 - tiny machine learning techniques for constrained devices (17 resultados)
Tiny Machine Learning Techniques for Constrained Devices
El-makkaoui, Khalid (EDT); Lamaakal, Ismail (EDT); Ouahbi, Ibrahim (EDT); Maleh, Yassine (EDT); El-latif, Ahmed A. Abd (EDT)
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 130,69
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Tiny Machine Learning Techniques for Constrained Devices
El-makkaoui, Khalid (EDT); Lamaakal, Ismail (EDT); Ouahbi, Ibrahim (EDT); Maleh, Yassine (EDT); El-latif, Ahmed A. Abd (EDT)
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 142,72
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 137,26
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Tiny Machine Learning Techniques for Constrained Devices
El-makkaoui, Khalid (EDT); Lamaakal, Ismail (EDT); Ouahbi, Ibrahim (EDT); Maleh, Yassine (EDT); El-latif, Ahmed A. Abd (EDT)
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 131,70
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Tiny Machine Learning Techniques for Constrained Devices
El-makkaoui, Khalid (EDT); Lamaakal, Ismail (EDT); Ouahbi, Ibrahim (EDT); Maleh, Yassine (EDT); El-latif, Ahmed A. Abd (EDT)
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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EUR 131,01
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 146,47
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Librería: THE SAINT BOOKSTORE, Southport, Reino UnidoTHE SAINT BOOKSTORE
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EUR 131,71
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Hardback. Condición: New. New copy - Usually dispatched within 4 working days.
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Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
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EUR 155,61
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HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
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EUR 152,45
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HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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EUR 157,92
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Condición: New.
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 194,08
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Hardcover. Condición: Brand New. 248 pages. 9.18x6.12x9.45 inches. In Stock.
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 145,06
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Hardcover. Condición: 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 comprehe…nsive 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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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 153,68
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Condición: New. PRINT ON DEMAND.
Tiny Machine Learning Techniques for Constrained Devices
Khalid El-Makkaoui|Ismail Lamaakal|Ibrahim Ouahbi|Yassine Maleh|Ahmed A. Abd El-Latif
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Librería: moluna, Greven, Alemaniamoluna
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EUR 130,00
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Condición: 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 cybers…ecurity and artificial intelli.
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Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
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EUR 160,72
Envío por EUR 43,25Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Hardcover. Condición: 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 comprehe…nsive 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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Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 183,41
Envío por EUR 31,69Se envía de Australia a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Hardcover. Condición: 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 comprehe…nsive 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.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 203,00
Envío por EUR 30,50Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Buch. Condición: 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. It is a guide to designing…, optimizing, securing, and applying TinyML models in real-world constrained environments.
