Handling Exploding Gradients in Machine Learning

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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[shyama]

I like his delivery - slow and clear - almost perfect answering machine.

[bhushanghadge]

Hey!
As per my understanding, skip connections help in vanishing gradient problem and not in exploding gradient problem. Since during backprop, strong gradient gets bypassed through the residual connection and it gets added to the regular weak gradient coming through the hidden layers. For exploding gradients, skip connection doesn’t help, since you add both the gradients in the backprop. So you actually add exploded gradient and skipped gradient which increases the gradient. So let me know if this is wrong.