Definition
In deep learning engineering, clipping is commonly applied to gradients to mitigate the exploding gradient problem, ensuring stable backpropagation. It can also refer to limiting output logits before applying softmax to prevent extreme probability distributions. By capping values within a predefined range, clipping improves model robustness and convergence speed, serving as a critical regularization step in training complex architectures like RNNs and Transformers.
Summary
Clipping is a technique used to limit the magnitude of values, such as gradients or output probabilities, to prevent numerical instability during training.
Key Concepts
- Gradient Clipping
- Numerical Stability
- Exploding Gradients
- Regularization
Use Cases
- Training recurrent neural networks
- Stabilizing transformer training
- Preventing loss divergence