What is Gradient Clipping for Neural Networks?

Unstable gradients pose significant challenges in neural networks, particularly in recurrent neural networks (RNNs) due to their recurrent structure. A common issue faced is the exploding gradients problem, which can disrupt the training process. Gradient Clipping is a technique introduced to address this problem by limiting the size of the gradients during training, helping to stabilize the network and ensure more effective learning.

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