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In this webcast, the presenter provides both theoretical and experimental evidence demonstrating the achievement of linear speedups on multi-core workstations on several benchmark optimization problems. Stochastic Gradient Descent (SGD) is a popular optimization algorithm for solving data-driven machine learning problems such as classification, model selection, sequence labeling, and recommendation.
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