--- title: What is an Epoch? description: What is an Epoch? --- [Skip to content](https://help.datasciencedojo.com/migration/what-is-an-epoch#main-content) English Show submenu for translations [Customer portal](https://help.datasciencedojo.com/tickets-view?hsLang=en) ![DSD main logo.png\]](https://help.datasciencedojo.com/hs-fs/hubfs/DSD%20main%20logo.png?height=40&name=DSD%20main%20logo.png) - [Default HubSpot Blog](https://help.datasciencedojo.com/blog) Open main navigation Close main navigation - [Default HubSpot Blog](https://help.datasciencedojo.com/blog) - English Show submenu for translations - [Customer portal](https://help.datasciencedojo.com/tickets-view) - Go to datasciencedojo.com Go to datasciencedojo.com How can we help? - There are no suggestions because the search field is empty. 1. [Help Center](https://help.datasciencedojo.com/migration?hsLang=en) 2. [Machine Learning](https://help.datasciencedojo.com/migration/machine-learning?hsLang=en) # What is an Epoch? The number of epochs is a hyperparameter that defines the number times that the learning algorithm will work through the entire training dataset.   One epoch means that each sample in the training dataset has had an opportunity to update the internal model parameters. An epoch is comprised of one or more batches. For example, as above, an epoch that has one batch is called the batch gradient descent learning algorithm.   You can think of a for-loop over the number of epochs where each loop proceeds over the training dataset. Within this for-loop is another nested for-loop that iterates over each batch of samples, where one batch has the specified “batch size” number of samples.   The number of epochs is traditionally large, often hundreds or thousands, allowing the learning algorithm to run until the error from the model has been sufficiently minimized. 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