parallel and distributed gradient descent

Parallel and distributed gradient descent refers to an optimization algorithm that leverages multiple processors or computers to accelerate the training of machine learning models. It divides the dataset and computes the gradients of the model parameters on different machines in parallel, allowing for faster convergence.

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