Questions tagged [mini-batch-gradient-descent]
Is a variation of the gradient descent algorithm that splits the training dataset into small batches that are used to calculate model error and update model coefficients. Implementations may choose to sum the gradient over the mini-batch which further reduces the variance of the gradient. The point of using mini-batch is that you are able to update your weights more than once each epoch, so your model gets better. Mini-batch is considered more efficient.