Intermediate · Systems
Gradient clipping
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A technique to prevent exploding gradients during neural network training by capping the magnitude of gradients that exceed a certain threshold.
Technical Definition
A technique to prevent exploding gradients during neural network training by capping the magnitude of gradients that exceed a certain threshold.
How it works
Gradient clipping is a crucial regularization technique used in training deep neural networks, particularly recurrent neural networks, to prevent the problem of exploding gradients. Exploding gradients occur when gradient values become excessively large, causing unstable updates to the model's weights and hindering convergence. Clipping limits the maximum norm or value of the gradients, ensuring that the updates remain within a manageable range and promoting more stable training.
Related Concepts
- Neural Network — A computing system inspired by biological neural networks that learns patterns from data through interconnected layers of nodes.
- Gradient Descent — An optimization algorithm that iteratively adjusts model parameters by moving in the direction of steepest decrease of the loss function.
- Backpropagation — An algorithm that efficiently computes gradients by propagating errors backward through the network using the chain rule.