Deep Learning, Pytorch, Fine Tuning, Computer Vision, Transfer Learning.
factor used by the scaler for the next iteration. It helps prevent underflow or overflow issues by dynamically adjusting the scale based on the gradients magnitude. It is called after scaler.step(optimizer). The purpose of using GradScaler() and the associated methods is to mitigate potential