Optimization Algorithms for Distributed Machine Learning: Synthesis Lectures on Learning, Networks, and Algorithms
Autor Gauri Joshien Limba Engleză Hardback – 26 noi 2022
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Specificații
ISBN-13: 9783031190667
ISBN-10: 3031190661
Pagini: 127
Ilustrații: XIII, 127 p. 40 illus., 38 illus. in color.
Dimensiuni: 168 x 240 mm
Greutate: 0.39 kg
Ediția:1st ed. 2023
Editura: Springer International Publishing
Colecția Springer
Seria Synthesis Lectures on Learning, Networks, and Algorithms
Locul publicării:Cham, Switzerland
ISBN-10: 3031190661
Pagini: 127
Ilustrații: XIII, 127 p. 40 illus., 38 illus. in color.
Dimensiuni: 168 x 240 mm
Greutate: 0.39 kg
Ediția:1st ed. 2023
Editura: Springer International Publishing
Colecția Springer
Seria Synthesis Lectures on Learning, Networks, and Algorithms
Locul publicării:Cham, Switzerland
Cuprins
Distributed Optimization in Machine Learning.- Calculus, Probability and Order Statistics Review.- Convergence of SGD and Variance-Reduced Variants.- Synchronous SGD and Straggler-Resilient Variants.- Asynchronous SGD and Staleness-Reduced Variants.- Local-update and Overlap SGD.- Quantized and Sparsified Distributed SGD.-
Decentralized SGD and its Variants.
Notă biografică
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).
Caracteristici
Discusses state-of-the-art algorithms that are at the core of the field of federated learning Analyzes each algorithm based on its error versus iterations convergence, and the runtime spent per iteration Provides insight into how the communication and synchronization protocol affects their practical performance