Review Batch

A batch is a group of examples or requests processed together, often to make training or inference more efficient.

A batch is the set of examples a training process handles before it updates a model’s parameters. Batch size is the number of examples in that set.

Batch size affects memory use, training speed, and the variability of each update. It is a training choice, not just a way to organize files.

With a batch size of 32, the system processes 32 training examples, calculates their combined loss, and then updates the model once.

A batch is not the entire dataset unless full-batch training is being used. Most modern training uses many smaller batches during each epoch.

One batch, one parameter update.

* [[training|Training]] * [[epoch|Epoch]] * [[dataset|Dataset]] * [[loss-function|Loss Function]]

* [Google for Developers, “Machine Learning Glossary”](https://developers.google.com/machine-learning/glossary) — provides established boundaries for core machine-learning vocabulary.

* Compare [[training|Training]], [[epoch|Epoch]], [[dataset|Dataset]], and [[loss-function|Loss Function]] to place Batch in the surrounding system. * Follow the cited source material above for the method, standard, or research context behind this draft.