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1. |
Record Nr. |
UNINA9910465714503321 |
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Titolo |
The dyer's handbook : memoirs on dyeing by a French gentleman-clothier in the age of enlightenment translated and contextualised / / edited by Dominique Cardon |
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Pubbl/distr/stampa |
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Oxford, [England] ; ; Philadelphia, [Pennsylvania] : , : Oxbow Books, , 2016 |
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©2016 |
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ISBN |
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Descrizione fisica |
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1 online resource (361 pages) : illustrations, tables |
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Collana |
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Ancient Textiles Series ; ; Volume 26 |
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Disciplina |
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Soggetti |
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Dyes and dyeing - Textile fibers - France - Languedoc - History - 18th century |
Dyes and dyeing - Wool - France - Languedoc - History - 18th century |
Dyes and dyeing |
Manuscripts, French - France - Languedoc |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references. |
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2. |
Record Nr. |
UNINA9910645947203321 |
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Titolo |
Machine learning under resource constraints . Volume 1 Fundamentals / / edited by Katharina Morik and Peter Marwedel |
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Pubbl/distr/stampa |
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Berlin ; ; Boston : , : De Gruyter, , 2022 |
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Descrizione fisica |
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1 online resource (xi, 489 pages) : illustrations |
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Collana |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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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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