Mini toolsDefinition
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 works by scaling down gradient values when they exceed a predetermined threshold. The clipping can be applied to individual gradients or the global norm of all gradients. This technique is particularly important in recurrent neural networks and deep architectures where gradient values can grow exponentially during backpropagation.
Use Cases
Training deep neural networks Recurrent neural networks Natural language processing models Deep reinforcement learning
