Dropout
A regularization technique that randomly deactivates neurons during training to prevent overfitting and improve generalization.
Related Concepts
- Regularization: Explore how Regularization relates to Dropout
- Overfitting: Explore how Overfitting relates to Dropout
- Ensemble Learning: Explore how Ensemble Learning relates to Dropout
Why It Matters
Understanding Dropout is crucial for anyone working with neural networks & deep learning. This concept helps build a foundation for more advanced topics in AI and machine learning.
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This term is part of the comprehensive AI/ML glossary. Explore related terms to deepen your understanding of this interconnected field.
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Related Terms
Ensemble Learning
Combining multiple models to produce better predictions than any individual model (bagging, boosting, stacking).
Overfitting
When a model learns training data too well, including noise and outliers, causing poor generalization to new data.
Regularization
Techniques to prevent overfitting by adding constraints or penalties to the model (L1, L2, dropout, early stopping).