Gradient Clipping

A deep learning training technique that prevents the exploding gradient problem by constraining the norm or absolute value of gradient vectors, thereby ensuring training stability. It is commonly applied during the training of recurrent architectures such as RNNs and LSTMs, as well as Transformer-based models.