Cookie Consent
Hi, this website uses essential cookies to ensure its proper operation and tracking cookies to understand how you interact with it. The latter will be set only after consent.
Read our Privacy Policy


An epoch is the term used for one full cycle through the entire training dataset during the training of a machine learning model.

How it works

During an epoch, the model's weights are updated to minimize the error in prediction. The number of epochs is a hyperparameter that defines the total number of times the learning algorithm will work through the entire training dataset.

The number of epochs is essentially the number of times the process of forward and backward propagation is repeated.

Here, too many epochs can lead to overfitting of the training dataset, whereas too few epochs may result in underfitting. Hence, the selection of the number of epochs is usually done through trial and error.

Keep in mind that one epoch is too big to feed to the computer at once so we divide it in several smaller batches. The number of epochs means how many times the learning algorithm will pass through the entire training dataset.

Lakera LLM Security Playbook
Learn how to protect against the most common LLM vulnerabilities

Download this guide to delve into the most common LLM security risks and ways to mitigate them.

Related terms
untouchable mode.
Get started for free.

Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.

Join our Slack Community.

Several people are typing about AI/ML security. 
Come join us and 1000+ others in a chat that’s thoroughly SFW.