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Machine Learning under Resource Constraints - Fundamentals

Engelsk Paperback

Machine Learning under Resource Constraints - Fundamentals

Engelsk Paperback
Tjek vores konkurrenters priser

Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering.

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.

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kr 1.462
Fragt: 39 kr
6 - 8 hverdage
20 kr
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Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering.

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 505
ISBN-13: 9783110785937
Indbinding: Paperback
Udgave:
ISBN-10: 3110785935
Kategori: Computer aided manufacture (CAM)
Udg. Dato: 31 dec 2022
Længde: 0mm
Bredde: 170mm
Højde: 240mm
Forlag: De Gruyter
Oplagsdato: 31 dec 2022
Forfatter(e):
Forfatter(e)

Kategori Computer aided manufacture (CAM)

ISBN-13 9783110785937

Sprog Engelsk

Indbinding Paperback

Sider 505

Udgave

Længde 0mm

Bredde 170mm

Højde 240mm

Udg. Dato 31 dec 2022

Oplagsdato 31 dec 2022

Forlag De Gruyter

Kategori sammenhænge