Gradient clipping is a technique used to prevent the exploding gradient problem in neural networks by limiting the size of the gradients during training. This method helps to stabilize the learning process, particularly in deep networks and recurrent neural networks, where large gradients can lead to instability and ineffective training. By constraining gradients to a specific threshold, gradient clipping ensures more consistent updates and improves convergence rates.
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