Gradient Clipping
Gradient clipping is a technique used in machine learning, particularly in deep learning, to prevent exploding gradients during neural network training. It works by scaling down gradients when their norm exceeds a predefined threshold, ensuring stable optimization and preventing numerical instability. This method is commonly applied in training recurrent neural networks (RNNs) and other architectures prone to gradient issues.
Developers should use gradient clipping when training deep neural networks, especially RNNs, LSTMs, or transformers, where long sequences or deep architectures can cause gradients to grow exponentially, leading to training divergence or NaN errors. It is essential for stabilizing training in reinforcement learning, natural language processing, and time-series models, as it allows for larger learning rates and faster convergence without compromising model performance.