A point-in-time snapshot of a model's weights, which is a crucial concept in model training. Usually, during the training process, a checkpoint is captured at the end of each epoch. This is done because if the model's performance deteriorates due to overfitting later on, these checkpoints allow you to revert to a previously saved state of the model, ensuring that you don't have to start the training process from scratch.
