The exploding gradient problem occurs when gradients used in training neural networks, particularly recurrent neural networks (RNNs), grow exponentially large, causing instability during model training. This issue can lead to weights being updated too drastically, resulting in divergence and preventing the model from learning effectively. Understanding this problem is crucial for effectively training RNNs, as it affects how they handle long-range dependencies in sequential data.
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