Vanishing and exploding gradients are issues that can occur during the training of deep neural networks in artificial intelligence. These problems arise from the way gradients (derivatives of the loss function with respect to the model’s parameters) are propagated backward through the layers of a deep network during the training process.
Vanishing gradients refer to a situation where the gradients become extremely small as they are propagated backward through the layers of a deep network. This can lead to slow or stagnant learning, as the updates to the model’s parameters become negligible, and the network struggles to learn meaningful representations from the data. Vanishing gradients are particularly problematic in very deep networks or when using certain activation functions, hindering the convergence of the training process and limiting the network’s ability to capture complex relationships within the data.
On the other hand, exploding gradients occur when the gradients grow excessively as they propagate backward through the layers. This can cause the model’s parameters to be updated too drastically, leading to unstable training and causing the loss function to fluctuate wildly. Exploding gradients can result in the model failing to converge or even diverging, rendering the training process ineffective. Both vanishing and exploding gradients can be mitigated through careful architecture design, appropriate weight initialization techniques, and the use of gradient clipping methods that limit the magnitude of gradients during training.
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