A deep learning model is overfitting to the training data, capturing noise and making it perform poorly on the validation set. Which technique might be employed to address this problem?
- Regularization Techniques
- Data Augmentation
- Gradient Descent Algorithms
- Hyperparameter Tuning
Regularization techniques, like L1 or L2 regularization, are used to prevent overfitting by adding penalties to the model's complexity, encouraging it to generalize better and avoid capturing noise.
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