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Explain The Following Three Variants Of Gradient Descent: Batch, Stochastic And Mini-batch? |
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Answer» Stochastic Gradient Descent: Uses only single training example to calculate the gradient and update PARAMETERS. Batch Gradient Descent: Calculate the gradients for the whole dataset and perform just one update at each ITERATION. Mini-batch Gradient Descent: Mini-batch gradient is a variation of stochastic gradient descent where INSTEAD of single training example, mini-batch of samples is USED. It’s one of the most popular OPTIMIZATION algorithms. Stochastic Gradient Descent: Uses only single training example to calculate the gradient and update parameters. Batch Gradient Descent: Calculate the gradients for the whole dataset and perform just one update at each iteration. Mini-batch Gradient Descent: Mini-batch gradient is a variation of stochastic gradient descent where instead of single training example, mini-batch of samples is used. It’s one of the most popular optimization algorithms. |
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