Articles liés à Optimization Algorithms for Distributed Machine Learning
Synopsis
This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Gauri Joshi, Ph.D., is an Associate Professor in the ECE department at Carnegie Mellon University. Dr. Joshi completed her Ph.D. from MIT EECS. Her current research is on designing algorithms for federated learning, distributed optimization, and parallel computing. Her awards and honors include being named as one of MIT Technology Review's 35 Innovators under 35 (2022), the NSF CAREER Award (2021), the ACM SIGMETRICS Best Paper Award (2020), Best Thesis Prize in Computer science at MIT (2012), and Institute Gold Medal of IIT Bombay (2010).
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurSpringer
- Date d'édition2023
- ISBN 10 3031190696
- ISBN 13 9783031190698
- ReliureBroché
- Langueanglais
- Nombre de pages144
- Coordonnées du fabricantnon disponible
Acheter neuf
Afficher cet article
EUR 42,96
EUR 9,70 expédition depuis Allemagne vers France
Destinations, frais et délaisAutres éditions populaires du même titre
Résultats de recherche pour Optimization Algorithms for Distributed Machine Learning
Image fournie par le vendeur
Optimization Algorithms for Distributed Machine Learning
Edité par
Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10 : 3031190696
ISBN 13 : 9783031190698
Neuf
Kartoniert / Broschiert
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where th. N° de réf. du vendeur 1215161127
Quantité disponible : Plus de 20 disponibles
Image d'archives
Optimization Algorithms for Distributed Machine Learning (Synthesis Lectures on Learning, Networks, and Algorithms)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. In. N° de réf. du vendeur ria9783031190698_new
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Optimization Algorithms for Distributed Machine Learning
Edité par
Springer International Publishing, Springer Nature Switzerland, 2023
ISBN 10 : 3031190696
ISBN 13 : 9783031190698
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime. N° de réf. du vendeur 9783031190698
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Optimization Algorithms for Distributed Machine Learning
ISBN 10 : 3031190696
ISBN 13 : 9783031190698
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime. 144 pp. Englisch. N° de réf. du vendeur 9783031190698
Quantité disponible : 2 disponible(s)
Image d'archives
Optimization Algorithms for Distributed Machine Learning (Synthesis Lectures on Learning, Networks, and Algorithms)
Vendeur : California Books, Miami, FL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur I-9783031190698
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Optimization Algorithms for Distributed Machine Learning
ISBN 10 : 3031190696
ISBN 13 : 9783031190698
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware -This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 144 pp. Englisch. N° de réf. du vendeur 9783031190698
Quantité disponible : 2 disponible(s)
Image d'archives
Optimization Algorithms for Distributed Machine Learning
Edité par
Springer-Nature New York Inc, 2023
ISBN 10 : 3031190696
ISBN 13 : 9783031190698
Neuf
Paperback
Vendeur : Revaluation Books, Exeter, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : Brand New. 140 pages. 9.45x6.61x0.33 inches. In Stock. N° de réf. du vendeur x-3031190696
Quantité disponible : 2 disponible(s)