Description: Exploding gradients are a phenomenon that occurs during the training of neural networks, especially in deep architectures like recurrent neural networks (RNNs) and deep feedforward networks. This phenomenon manifests when gradients, which are the derivatives of the loss function with respect to the model weights, become extremely large. As a result, updates to the model weights become disproportionately large, which can lead the model to diverge instead of converging to an optimal solution. This problem is particularly prevalent in networks with many layers, where the backpropagation of gradients can be amplified as it moves through the layers. Exploding gradients can cause weight values to become infinite or NaN (not a number), disrupting the training process. To mitigate this issue, several techniques have been developed, such as gradient clipping and the use of activation functions that limit the range of gradients. Understanding and managing exploding gradients is crucial for the effective design of deep learning models, as their presence can significantly affect the performance and stability of models during training.
