Advanced · Neural Networks
Exploding gradient problem
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Gradients become excessively large during neural network training, hindering learning.
Technical Definition
Gradients become excessively large during neural network training, hindering learning.
How it works
The exploding gradient problem is a challenge in training deep neural networks, particularly recurrent ones, where gradients become extremely large. This causes drastic updates to the network's weights, making it unstable and difficult or impossible to train effectively. Gradient clipping is a common technique to mitigate this issue.
Related Concepts
- Neural Network — A computing system inspired by biological neural networks that learns patterns from data through interconnected layers of nodes.
- Recurrent Neural Network (RNN) — A neural network with loops that maintain hidden state, designed to process sequential data like text and time series.
- Gradient clipping — A technique to prevent exploding gradients during neural network training by capping the magnitude of gradients that exceed a certain threshold.