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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Similar Concepts
- accelerated gradient descent methods
- adaptive learning rates in gradient descent
- batch gradient descent
- conjugate gradient descent
- convergence of gradient descent
- gradient descent for linear regression
- gradient descent for neural networks
- hybrid optimization algorithms combining gradient descent
- loop parallelization
- mini-batch gradient descent
- online gradient descent
- parallel thinking
- proximal gradient descent
- stochastic gradient descent
- variants of gradient descent algorithms