What is the purpose of gradient clipping in neural networks?

Gradient clipping is primarily utilized to prevent exploding gradients during the training of neural networks. This phenomenon occurs particularly in deep networks or recurrent neural networks where the gradients can exponentially grow, leading to instability and making it difficult for the model to converge.

By applying gradient clipping, the gradients are restricted to a certain threshold, ensuring that they do not become excessively large. This helps maintain stable training dynamics, allowing for more effective optimization and ultimately contributing to the model's ability to learn. Clipping the gradients ensures that updates to the model weights remain within a manageable range, thereby preventing drastic adjustments that could derail the learning process.

In contrast, reducing overfitting typically involves techniques such as regularization, dropout, or early stopping, rather than gradient clipping. Speeding up training can involve various optimization strategies like using different algorithms (e.g., Adam or RMSprop) or batch normalization, which are not directly related to gradient clipping. Finally, enhancing feature selection pertains to identifying the most relevant features in the data rather than addressing issues related to the size of the gradients during training.