Gradient Clipping

Gradient clipping is a technique used in training machine learning models, particularly deep neural networks, to prevent the gradients from becoming excessively large during the optimization process. It involves setting a threshold value; if the computed gradients exceed this threshold, they are scaled down to maintain stability in training. This helps to avoid issues such as exploding gradients, which can lead to numerical instability and hinder convergence. Gradient clipping is commonly used in recurrent neural networks (RNNs) and other deep learning architectures where gradients can grow rapidly. By ensuring that gradients remain within a manageable range, gradient clipping contributes to more efficient and reliable training processes.