Mini-batch: Difference between revisions

8,384 bytes added ,  28 February 2023
Created page with "{{see also|Machine learning terms}} ==Introduction== Mini-batch training is a machine learning technique used to efficiently train large datasets. This division of the entire dataset into smaller batches allows for faster training as well as improved convergence of the model to its optimal solution. ==Theoretical Background== Traditional machine learning relies on batch gradient descent to train the model on all data in one iteration. Unfortunately, when the dataset gro..."
(Created page with "{{see also|Machine learning terms}} ==Introduction== Mini-batch training is a machine learning technique used to efficiently train large datasets. This division of the entire dataset into smaller batches allows for faster training as well as improved convergence of the model to its optimal solution. ==Theoretical Background== Traditional machine learning relies on batch gradient descent to train the model on all data in one iteration. Unfortunately, when the dataset gro...")
(No difference)