Walle
Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, a '''checkpoint''' refers to a snapshot of the current state of a model during the training process. Checkpoints are primarily used for saving the model's weights and architecture, and sometimes additional information such as learning rates and optimizer states, at regular intervals or after a specified number of iterations. This allows the training process to be resumed from a previous state in..."
