What is the significance of the vanishing gradient problem in training deep neural networks?
- It causes exploding gradients, making training unstable.
- It is not a significant issue in deep learning.
- It leads to faster convergence during training.
- It prevents models from overfitting.
The vanishing gradient problem is a critical issue in deep learning. When gradients become too small during backpropagation, it hinders the training process. It doesn't lead to faster convergence, nor does it prevent overfitting.
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