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Machine Learning Commodity Tiny by Guo Song (27 results)
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Condition: As New. Unread book in perfect condition.
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Condition: New.
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Condition: New. pages cm.
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Machine Learning on Commodity Tiny Devices : Theory and Practice
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Machine Learning on Commodity Tiny Devices: Theory and Practice
Language: English
Published by Taylor & Francis Ltd, 2024
ISBN 10: 1032374268 ISBN 13: 9781032374260
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days.
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Machine Learning on Commodity Tiny Devices : Theory and Practice
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Machine Learning on Commodity Tiny Devices
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 59.57
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Condition: New. pages cm.
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Condition: As New. Unread book in perfect condition.
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Paperback. Condition: Brand New. 268 pages. 10.00x7.00x10.00 inches. In Stock.
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Condition: New.
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Condition: New.
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Machine Learning on Commodity Tiny Devices : Theory and Practice
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Machine Learning on Commodity Tiny Devices: Theory and Practice
Language: English
Published by Taylor & Francis Ltd, 2022
ISBN 10: 1032374233 ISBN 13: 9781032374239
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. New copy - Usually dispatched within 4 working days.
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Machine Learning on Commodity Tiny Devices : Theory and Practice
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Machine Learning on Commodity Tiny Devices
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 92.72
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Taschenbuch. Condition: Neu. Machine Learning on Commodity Tiny Devices | Theory and Practice | Song Guo (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | CRC Press | EAN 9781032374260 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu.
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Hardcover. Condition: Brand New. 249 pages. 10.00x7.00x0.75 inches. In Stock.
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Machine Learning on Commodity Tiny Devices
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
£ 60.47
£ 4.16 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketPAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Machine Learning on Commodity Tiny Devices
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization and hardware-level instruction acceleration.Analyzing the limitations of conventional in-cloud computing would reveal that on-device learning is a promising research direction to meet the requirements of edge intelligence applications. As to the cutting-edge research of TinyML, implementing a high-efficiency learning framework and enabling system-level acceleration is one of the most fundamental issues. This book presents a comprehensive discussion of the latest research progress and provides system-level insights on designing TinyML frameworks, including neural network design, training algorithm optimization and domain-specific hardware acceleration. It identifies the main challenges when deploying TinyML tasks in the real world and guides the researchers to deploy a reliable learning system.This book will be of interest to students and scholars in the field of edge intelligence, especially to those with sufficient professional Edge AI skills. It will also be an excellent guide for researchers to implement high-performance TinyML systems. 268 pp. Englisch.
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HRD. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Machine Learning on Commodity Tiny Devices
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 4 out of 5 stars
£ 94.36
£ 5.02 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Condition: New. PRINT ON DEMAND.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization and hardware-level instruction acceleration.Analyzing the limitations of conventional in-cloud computing would reveal that on-device learning is a promising research direction to meet the requirements of edge intelligence applications. As to the cutting-edge research of TinyML, implementing a high-efficiency learning framework and enabling system-level acceleration is one of the most fundamental issues. This book presents a comprehensive discussion of the latest research progress and provides system-level insights on designing TinyML frameworks, including neural network design, training algorithm optimization and domain-specific hardware acceleration. It identifies the main challenges when deploying TinyML tasks in the real world and guides the researchers to deploy a reliable learning system.This book will be of interest to students and scholars in the field of edge intelligence, especially to those with sufficient professional Edge AI skills. It will also be an excellent guide for researchers to implement high-performance TinyML systems.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Song Guo is a Full Professor leading the Edge Intelligence Lab and Research Group of Networking and Mobile Computing at the Hong Kong Polytechnic University. Professor Guo is a Fellow of the Canadian Academy of Engineering, Fellow of the.
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. It presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization, and hardware-level instruction acceleration.
