What is gradient clipping and when is it used in neural networks?

Gradient clipping is a technique used to prevent the exploding gradients problem by capping the gradients during backpropagation to a specified threshold. When the calculated gradients exceed this threshold, they are scaled down to bring them back within the acceptable range. This is particularly useful in training deep recurrent neural networks (RNNs), where large gradients can lead to unstable learning and oscillations. By applying gradient clipping, the training process becomes more stable, allowing for better convergence.