Description: Gradient clipping is a technique used in training neural networks to prevent the problem of exploding gradients, which can occur during backpropagation. This phenomenon arises when gradients, which are the derivatives of the loss function with respect to the model parameters, become extremely large, leading to unstable weight updates and ultimately divergence of the learning process. Gradient clipping limits the value of gradients to a predefined threshold, ensuring they do not exceed a specific value. This is achieved by normalizing the gradients: if the norm of the gradient vector exceeds the threshold, the vector is scaled down to have a norm equal to the threshold. This technique is particularly relevant in deep learning models, including deep and recurrent neural networks, where the propagation of errors through multiple layers can amplify gradients. By implementing gradient clipping, the stability of training is improved, and convergence towards a local minimum of the loss function is facilitated, resulting in a more robust and efficient model.
