model checkpoint

A model checkpoint is a saved copy of the trained weights and biases of a neural network model at a specific point in time during the training process. It can be used to reload the model's weights and continue training from the point where it was saved, or to evaluate the performance of the model at that particular stage of training.

When initializing a model from scratch, a checkpoint is not needed as there are no pre-existing weights to be loaded. In such cases, the model's weights are usually randomly initialized using a specific method, such as Xavier initialization or He initialization, to avoid the issue of vanishing gradients and exploding gradients during training.

However, if you have a pre-trained model that you want to use as a starting point for fine-tuning or transfer learning, you can load the weights from a saved checkpoint file to initialize the model's weights. This can save time and resources compared to training the model from scratch.