Term
Gradient Clipping (ˈgrædɪənt ˈklɪpɪŋ)
Definition
Gradient clipping is a technique used in AI to prevent the issue of exploding gradients during training by setting a limit on their size. This maintains the numerical stability of the learning process.
Where you’ll find it
Gradient clipping is typically found in the settings or configuration panel of neural network training algorithms, particularly in frameworks dealing with deep learning like TensorFlow or PyTorch. It is commonly used in complex models, such as recurrent neural networks (RNNs).
Common use cases
- To stabilize training in deep learning models where the risk of exploding gradients is high.
- To ensure smooth and consistent training progress in models that are sensitive to large updates.
- To combine with other optimization techniques to improve the overall effectiveness and speed of the training process.
Things to watch out for
- Setting the clipping threshold too low can hinder the effectiveness of model training by not allowing sufficient gradient flow.
- It may not be necessary for simpler models or those that do not show signs of instability.
- Fine-tune the threshold specific to your model’s requirements to find the balance between stability and training dynamics.
Related terms
- Neural Networks
- Backpropagation
- Recurrent Neural Networks (RNN)
- Learning Rate
- Optimization Algorithms