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This segment addresses the issue of exploding gradients in neural network training, a phenomenon where gradient values increase exponentially, potentially destabilizing the learning process. It presents practical solutions such as gradient clipping to impose thresholds, implementing batch normalization to maintain stable gradient scales, and adjusting network architecture.

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