9783030740412 - hardware-aware probabilistic machine learning models: learning, inference and use cases di olascoaga, laura isabel galindez; meert, wannes; verhelst, marian (11 risultati)
Hardware-aware Probabilistic Machine Learning Models : Learning, Inference and Use Cases
Olascoaga, Laura Isabel Galindez; Meert, Wannes; Verhelst, Marian
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Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases
Galindez Olascoaga, Laura Isabel; Meert, Wannes; Verhelst, Marian
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Hardware-aware Probabilistic Machine Learning Models : Learning, Inference and Use Cases
Olascoaga, Laura Isabel Galindez; Meert, Wannes; Verhelst, Marian
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Hardware-aware Probabilistic Machine Learning Models : Learning, Inference and Use Cases
Olascoaga, Laura Isabel Galindez; Meert, Wannes; Verhelst, Marian
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Hardware-aware Probabilistic Machine Learning Models : Learning, Inference and Use Cases
Olascoaga, Laura Isabel Galindez; Meert, Wannes; Verhelst, Marian
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases
Galindez Olascoaga, Laura Isabel (Author)/ Meert, Wannes (Author)/ Verhelst, Marian (Author)
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Da: Revaluation Books, Exeter, , Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 175 pages. 9.25x6.10x0.59 inches. In Stock.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performa…nce of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover.The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumpt…ion and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover.The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering. 176 pp. Englisch.
Hardware-Aware Probabilistic Machine Learning Models
Laura Isabel Galindez Olascoaga|Wannes Meert|Marian Verhelst
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Da: moluna, Greven, , Germaniamoluna
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EUR 74,71
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Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces a new, systematic approach for the realization of hardware-awareness with probabilistic modelsEnables readers to accommodate various systems and applications, as demonstrated with multiple use ca…ses targeting distinct types of device.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 85,59
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption…and performance of the machine learning task, with the overarching goal of balancing the two optimally.The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover.The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 176 pp. Englisch.
