Gradient Normalization
Gradient normalization is a technique in machine learning and deep learning that involves scaling or adjusting the gradients during backpropagation to stabilize training and improve convergence. It is commonly used to prevent issues like exploding or vanishing gradients, particularly in recurrent neural networks (RNNs) and deep architectures. Methods include gradient clipping, which caps gradient values, and gradient scaling, which normalizes them to a target norm.
Developers should learn gradient normalization when training deep neural networks, especially RNNs, LSTMs, or transformers, to mitigate training instability and accelerate convergence. It is crucial in scenarios with long sequences or complex models where gradients can become too large or too small, leading to poor performance or non-convergence. Using techniques like gradient clipping can also enhance robustness in adversarial training or reinforcement learning applications.