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{{see also|machine learning terms}} | |||
[[Batch]] is the set of [[example]]s used in one [[training]] iteration. The [[batch size]] determines the number of examples in a batch. In [[machine learning]], batches are subsets of [[data]] used for training a [[model]]. The goal is to break up an expansive [[dataset]] into smaller, more manageable chunks for easier processing. | [[Batch]] is the set of [[example]]s used in one [[training]] iteration. The [[batch size]] determines the number of examples in a batch. In [[machine learning]], batches are subsets of [[data]] used for training a [[model]]. The goal is to break up an expansive [[dataset]] into smaller, more manageable chunks for easier processing. | ||
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The batch size is the number of examples used in each iteration of the training process. A larger batch size may provide faster progress but requires more memory and may not reach an optimal solution as quickly as desired. On the other hand, smaller batches offer better convergence and generalization rates. Ultimately, selecting a batch size depends on your available hardware as well as the specific problem being tackled. | The batch size is the number of examples used in each iteration of the training process. A larger batch size may provide faster progress but requires more memory and may not reach an optimal solution as quickly as desired. On the other hand, smaller batches offer better convergence and generalization rates. Ultimately, selecting a batch size depends on your available hardware as well as the specific problem being tackled. | ||
[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] | |||