What are some advanced techniques to prevent overfitting in a deep learning model?
- Regularization, Dropout, Early Stopping, Data Augmentation
- Regularization, Dropout, Early Stopping, Over-sampling
- Regularization, Dropout, Late Stopping, Data Augmentation
- Regularization, Over-sampling, Early Stopping, Data Reduction
Advanced techniques such as "Regularization, Dropout, Early Stopping, and Data Augmentation" help in preventing overfitting by adding constraints, randomly deactivating neurons, halting training, and expanding the dataset, respectively.
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