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Könyv Optimization Algorithms for Distributed Machine Learning Gauri Joshi

Optimization Algorithms for Distributed Machine Learning

Szerző: Gauri Joshi
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Springer, Berlin
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
17 032 Ft
This book discusses state-of-the-art stochastic optimization algorithms for distributed machine lear...

Információk a könyvről

Szerző
Gauri Joshi
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2023
oldal
127
EAN
9783031190698
Enbook ID
44406882
Kiadó
Springer, Berlin
Súly
240
Méretek
168 x 240

Teljes leírás

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.

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