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Condition: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.
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Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) -Language: spanish
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Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)
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Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) -Language: spanish
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Condition: New. 2024th edition NO-PA16APR2015-KAP.
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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Hardware Architectures
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Taschenbuch. Condition: Neu. Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing | Hardware Architectures | Sudeep Pasricha (u. a.) | Taschenbuch | xiv | Englisch | 2024 | Springer | EAN 9783031195709 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing : Hardware Architectures
Language: English
Published by Springer International Publishing, Springer International Publishing, 2024
ISBN 10: 3031195701 ISBN 13: 9783031195709
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits.
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Condition: New. 1st ed. 2024 edition NO-PA16APR2015-KAP.
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Condition: Hervorragend. Zustand: Hervorragend | Seiten: 428 | Sprache: Englisch | Produktart: Bücher | This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits.
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Add to basketHardcover. Condition: Brand New. 426 pages. 9.25x6.10x9.21 inches. In Stock.
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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing : Hardware Architectures
Language: English
Published by Springer International Publishing, Springer Nature Switzerland, 2023
ISBN 10: 3031195671 ISBN 13: 9783031195679
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits.
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Condition: New. 2024th edition NO-PA16APR2015-KAP.
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Taschenbuch. Condition: Neu. Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing | Software Optimizations and Hardware/Software Codesign | Sudeep Pasricha (u. a.) | Taschenbuch | xiv | Englisch | 2024 | Springer | EAN 9783031399343 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Condition: Neu. Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing | Use Cases and Emerging Challenges | Sudeep Pasricha (u. a.) | Taschenbuch | xv | Englisch | 2024 | Springer | EAN 9783031406799 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Use Cases and Emerging Challenges
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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Software Optimizations and Hardware/Software Codesign
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 234.36
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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing : Software Optimizations and Hardware/Software Codesign
Language: English
Published by Springer Nature Switzerland, Springer Nature Switzerland, 2024
ISBN 10: 303139934X ISBN 13: 9783031399343
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits.Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing;Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization;Describes real applications todemonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.
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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing : Use Cases and Emerging Challenges
Language: English
Published by Springer Nature Switzerland, Springer Nature Switzerland, 2024
ISBN 10: 3031406796 ISBN 13: 9783031406799
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advances towards thegoal ofenabling efficient implementation ofmachine learning models onresource-constrained systems, covering different application domains. Thefocus is onpresenting interesting and new use cases ofapplying machine learning toinnovative application domains, exploring theefficient hardware design ofefficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques forenergy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques forachieving even greater energy, reliability, and performance benefits.Discusses efficient implementation ofmachine learning in embedded, CPS, IoT, and edge computing;Offers comprehensive coverage ofhardware design, software design, and hardware/software co-design and co-optimization;Describes real applications todemonstrate how embedded, CPS, IoT, and edge applications benefit frommachine learning.
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Condition: New. pp. 588 1st ed. 2024 edition NO-PA16APR2015-KAP.
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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing : Use Cases and Emerging Challenges
Language: English
Published by Springer Nature Switzerland, Springer Nature Switzerland, 2023
ISBN 10: 3031406761 ISBN 13: 9783031406768
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advances towards thegoal ofenabling efficient implementation ofmachine learning models onresource-constrained systems, covering different application domains. Thefocus is onpresenting interesting and new use cases ofapplying machine learning toinnovative application domains, exploring theefficient hardware design ofefficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques forenergy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques forachieving even greater energy, reliability, and performance benefits.Discusses efficient implementation ofmachine learning in embedded, CPS, IoT, and edge computing;Offers comprehensive coverage ofhardware design, software design, and hardware/software co-design and co-optimization;Describes real applications todemonstrate how embedded, CPS, IoT, and edge applications benefit frommachine learning.
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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing : Software Optimizations and Hardware/Software Codesign
Language: English
Published by Springer Nature Switzerland, Springer International Publishing, 2023
ISBN 10: 3031399315 ISBN 13: 9783031399312
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits.Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing;Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization;Describes real applications todemonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.
