How can you prevent overfitting in a deep learning model developed with TensorFlow or PyTorch?
- Decrease the learning rate
- Increase the model complexity
- Use a smaller training dataset
- Use dropout layers
To prevent overfitting, using dropout layers is a common technique. Dropout layers randomly deactivate a fraction of neurons during training, which helps the model generalize better to new data.
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