Distributed stochastic gradient descent (DSGD) is a variant of the stochastic gradient descent optimization algorithm that is executed across multiple machines or nodes to accelerate the training of large-scale machine learning models. This method allows for parallel processing of data, which helps to overcome limitations related to memory and computation time, making it suitable for scalable machine learning algorithms and effective distributed training techniques.
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