Deep learning edge cloud based (12 résultats)
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Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-UniPBShop.store UK
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EUR 174,66
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Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
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EUR 202,61
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Etat : New.
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Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-UniPBShop.store UK
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EUR 212,31
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HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.
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Vendeur : California Books, Miami, FL, Etats-UnisCalifornia Books
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EUR 247,05
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Etat : New.
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Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-UnisGrand Eagle Retail
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EUR 185,18
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Paperback. Etat : new. Paperback. The Internet of Things (IoT) is transforming the way people, devices, and systems interact by creating highly connected environments that generate and exchange vast amounts of data in real time. From smart cities and healthcare to manufacturing, transportation, and industrial automation, IoT tec…hnologies are enabling more intelligent, responsive, and data-driven operations across nearly every sector. As these connected ecosystems continue to grow in scale and complexity, there is an increasing need for advanced technologies capable of extracting meaningful insights, supporting autonomous decision-making, and adapting to dynamic environments. Deep learning has emerged as a powerful solution, enabling IoT systems to perform sophisticated tasks such as prediction, classification, optimization, and intelligent control. At the same time, the convergence of cloud and edge computing is redefining how these capabilities are delivered, creating architectures that balance computational power, scalability, efficiency, and real-time responsiveness. Deep Learning in Edge and Cloud-Based IoT Systems examines the integration of deep learning with edge and cloud computing to support the next generation of intelligent IoT applications. This book explores the theoretical foundations, deployment strategies, optimization techniques, privacy and security considerations, and emerging applications of deep learning across distributed computing environments. Covering topics such as heterogeneous and real-time IoT data analytics, privacy preservation, and device-edge-cloud continuum, this book is a fundamental academic resource for graduate and doctoral students, IoT architects, software engineers, cloud engineers, technology developers, systems engineers, policymakers, and more. 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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Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-UnisGrand Eagle Retail
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EUR 225,04
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Hardcover. Etat : new. Hardcover. The Internet of Things (IoT) is transforming the way people, devices, and systems interact by creating highly connected environments that generate and exchange vast amounts of data in real time. From smart cities and healthcare to manufacturing, transportation, and industrial automation, IoT tec…hnologies are enabling more intelligent, responsive, and data-driven operations across nearly every sector. As these connected ecosystems continue to grow in scale and complexity, there is an increasing need for advanced technologies capable of extracting meaningful insights, supporting autonomous decision-making, and adapting to dynamic environments. Deep learning has emerged as a powerful solution, enabling IoT systems to perform sophisticated tasks such as prediction, classification, optimization, and intelligent control. At the same time, the convergence of cloud and edge computing is redefining how these capabilities are delivered, creating architectures that balance computational power, scalability, efficiency, and real-time responsiveness. Deep Learning in Edge and Cloud-Based IoT Systems examines the integration of deep learning with edge and cloud computing to support the next generation of intelligent IoT applications. This book explores the theoretical foundations, deployment strategies, optimization techniques, privacy and security considerations, and emerging applications of deep learning across distributed computing environments. Covering topics such as heterogeneous and real-time IoT data analytics, privacy preservation, and device-edge-cloud continuum, this book is a fundamental academic resource for graduate and doctoral students, IoT architects, software engineers, cloud engineers, technology developers, systems engineers, policymakers, and more. 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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Vendeur : CitiRetail, Stevenage, Royaume-UniCitiRetail
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EUR 183,72
EUR 43,14 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 1 disponible(s)
Paperback. Etat : new. Paperback. The Internet of Things (IoT) is transforming the way people, devices, and systems interact by creating highly connected environments that generate and exchange vast amounts of data in real time. From smart cities and healthcare to manufacturing, transportation, and industrial automation, IoT tec…hnologies are enabling more intelligent, responsive, and data-driven operations across nearly every sector. As these connected ecosystems continue to grow in scale and complexity, there is an increasing need for advanced technologies capable of extracting meaningful insights, supporting autonomous decision-making, and adapting to dynamic environments. Deep learning has emerged as a powerful solution, enabling IoT systems to perform sophisticated tasks such as prediction, classification, optimization, and intelligent control. At the same time, the convergence of cloud and edge computing is redefining how these capabilities are delivered, creating architectures that balance computational power, scalability, efficiency, and real-time responsiveness. Deep Learning in Edge and Cloud-Based IoT Systems examines the integration of deep learning with edge and cloud computing to support the next generation of intelligent IoT applications. This book explores the theoretical foundations, deployment strategies, optimization techniques, privacy and security considerations, and emerging applications of deep learning across distributed computing environments. Covering topics such as heterogeneous and real-time IoT data analytics, privacy preservation, and device-edge-cloud continuum, this book is a fundamental academic resource for graduate and doctoral students, IoT architects, software engineers, cloud engineers, technology developers, systems engineers, policymakers, and more. 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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Vendeur : CitiRetail, Stevenage, Royaume-UniCitiRetail
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 222,74
EUR 43,14 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 1 disponible(s)
Hardcover. Etat : new. Hardcover. The Internet of Things (IoT) is transforming the way people, devices, and systems interact by creating highly connected environments that generate and exchange vast amounts of data in real time. From smart cities and healthcare to manufacturing, transportation, and industrial automation, IoT tec…hnologies are enabling more intelligent, responsive, and data-driven operations across nearly every sector. As these connected ecosystems continue to grow in scale and complexity, there is an increasing need for advanced technologies capable of extracting meaningful insights, supporting autonomous decision-making, and adapting to dynamic environments. Deep learning has emerged as a powerful solution, enabling IoT systems to perform sophisticated tasks such as prediction, classification, optimization, and intelligent control. At the same time, the convergence of cloud and edge computing is redefining how these capabilities are delivered, creating architectures that balance computational power, scalability, efficiency, and real-time responsiveness. Deep Learning in Edge and Cloud-Based IoT Systems examines the integration of deep learning with edge and cloud computing to support the next generation of intelligent IoT applications. This book explores the theoretical foundations, deployment strategies, optimization techniques, privacy and security considerations, and emerging applications of deep learning across distributed computing environments. Covering topics such as heterogeneous and real-time IoT data analytics, privacy preservation, and device-edge-cloud continuum, this book is a fundamental academic resource for graduate and doctoral students, IoT architects, software engineers, cloud engineers, technology developers, systems engineers, policymakers, and more. 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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Vendeur : AussieBookSeller, Truganina, VIC, AustralieAussieBookSeller
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 245,12
EUR 31,92 expéditionExpédition depuis Australie vers Etats-UnisQuantité disponible : 1 disponible(s)
Paperback. Etat : new. Paperback. The Internet of Things (IoT) is transforming the way people, devices, and systems interact by creating highly connected environments that generate and exchange vast amounts of data in real time. From smart cities and healthcare to manufacturing, transportation, and industrial automation, IoT tec…hnologies are enabling more intelligent, responsive, and data-driven operations across nearly every sector. As these connected ecosystems continue to grow in scale and complexity, there is an increasing need for advanced technologies capable of extracting meaningful insights, supporting autonomous decision-making, and adapting to dynamic environments. Deep learning has emerged as a powerful solution, enabling IoT systems to perform sophisticated tasks such as prediction, classification, optimization, and intelligent control. At the same time, the convergence of cloud and edge computing is redefining how these capabilities are delivered, creating architectures that balance computational power, scalability, efficiency, and real-time responsiveness. Deep Learning in Edge and Cloud-Based IoT Systems examines the integration of deep learning with edge and cloud computing to support the next generation of intelligent IoT applications. This book explores the theoretical foundations, deployment strategies, optimization techniques, privacy and security considerations, and emerging applications of deep learning across distributed computing environments. Covering topics such as heterogeneous and real-time IoT data analytics, privacy preservation, and device-edge-cloud continuum, this book is a fundamental academic resource for graduate and doctoral students, IoT architects, software engineers, cloud engineers, technology developers, systems engineers, policymakers, and more. 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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Vendeur : preigu, Osnabrück, Allemagnepreigu
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 219,80
EUR 70,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 5 disponible(s)
Taschenbuch. Etat : Neu. Deep Learning in Edge and Cloud-Based IoT Systems | Uzair Aslam Bhatti (u. a.) | Taschenbuch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798260006160 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand….
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Vendeur : AussieBookSeller, Truganina, VIC, AustralieAussieBookSeller
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EUR 292,55
EUR 31,92 expéditionExpédition depuis Australie vers Etats-UnisQuantité disponible : 1 disponible(s)
Hardcover. Etat : new. Hardcover. The Internet of Things (IoT) is transforming the way people, devices, and systems interact by creating highly connected environments that generate and exchange vast amounts of data in real time. From smart cities and healthcare to manufacturing, transportation, and industrial automation, IoT tec…hnologies are enabling more intelligent, responsive, and data-driven operations across nearly every sector. As these connected ecosystems continue to grow in scale and complexity, there is an increasing need for advanced technologies capable of extracting meaningful insights, supporting autonomous decision-making, and adapting to dynamic environments. Deep learning has emerged as a powerful solution, enabling IoT systems to perform sophisticated tasks such as prediction, classification, optimization, and intelligent control. At the same time, the convergence of cloud and edge computing is redefining how these capabilities are delivered, creating architectures that balance computational power, scalability, efficiency, and real-time responsiveness. Deep Learning in Edge and Cloud-Based IoT Systems examines the integration of deep learning with edge and cloud computing to support the next generation of intelligent IoT applications. This book explores the theoretical foundations, deployment strategies, optimization techniques, privacy and security considerations, and emerging applications of deep learning across distributed computing environments. Covering topics such as heterogeneous and real-time IoT data analytics, privacy preservation, and device-edge-cloud continuum, this book is a fundamental academic resource for graduate and doctoral students, IoT architects, software engineers, cloud engineers, technology developers, systems engineers, policymakers, and more. 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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Vendeur : preigu, Osnabrück, Allemagnepreigu
Contacter le vendeurVendeur avec une évaluation de 5 étoilesEtat: Neuf
EUR 261,70
EUR 70,00 expéditionExpédition depuis Allemagne vers Etats-UnisQuantité disponible : 5 disponible(s)
Buch. Etat : Neu. Deep Learning in Edge and Cloud-Based IoT Systems | Uzair Aslam Bhatti (u. a.) | Buch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798260006153 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
