Gradient clipping is a technique that limits gradient values during training to prevent excessively large parameter updates. It is commonly used to mitigate the exploding gradient problem in deep and recurrent neural networks.
A commonly used mechanism to mitigate the exploding gradient problem by artificially limiting (clipping) the maximum value of gradients when using gradient descent to train a model.