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What do you understand about gradient clipping in the context of deep learning?

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Gradient Clipping is a technique for dealing with the problem of exploding gradients (a situation in which huge error gradients build up over time, resulting in massive modifications to neural network model weights during training) that happens during backpropagation. The problem of exploding gradients occurs when the gradients get excessively big during training, causing the model to become unstable. If the gradient has crossed the anticipated range, the gradient values are driven element-by-element to a specific minimum or maximum value. Gradient clipping improves numerical stability while training a neural network, but it has little effect on the performance of the model.

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