Gradient Descent
Machine Learning
A technique that prevents exploding gradients by limiting their magnitude during training. This helps maintain stable training in deep neural networks.
Detailed Explanation
Gradient clipping is a technique used in machine learning to prevent exploding gradients by capping their magnitude during backpropagation. This stabilizes the training process, especially for deep neural networks, ensuring gradients do not become excessively large, which can cause unstable updates or divergence. Implementing gradient clipping helps improve convergence and training performance in complex models.
Use Cases
- •Use case: Stabilizing training of deep neural networks by preventing gradient explosion during backpropagation.
Related Terms
Other terms in the Machine Learning category