Intermediate · Neural Networks
Dropout regularization
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A technique that randomly deactivates neurons during training to prevent overfitting.
Technical Definition
A technique that randomly deactivates neurons during training to prevent overfitting.
How it works
Dropout regularization is a technique used to prevent overfitting in neural networks. During training, a random subset of neurons and their connections are temporarily ignored for each training step. This forces the network to learn more robust features by preventing co-adaptation of neurons, effectively training an ensemble of smaller networks.
Related Concepts
- Neural Network — A computing system inspired by biological neural networks that learns patterns from data through interconnected layers of nodes.
- Overfitting — When a model learns noise and specific patterns in training data too well, causing it to perform poorly on new, unseen data.
- Regularization — Techniques that constrain a model's complexity to prevent overfitting and improve generalization to unseen data.
- Training — The process of adjusting a model's parameters so it learns patterns from labeled or unlabeled data.