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I'm facing overfitting issues in my deep learning model. What techniques have helped you prevent this?
To prevent overfitting, I use dropout layers, apply L2 regularization, and implement early stopping during training.I also ensure the model isn’t overly complex and train it with data augmentation or more data when possible.Monitoring validation loss helps me fine-tune these strategies effectively.
To prevent overfitting, I use dropout layers, apply L2 regularization, and implement early stopping during training.
See lessI also ensure the model isn’t overly complex and train it with data augmentation or more data when possible.
Monitoring validation loss helps me fine-tune these strategies effectively.
How do you decide between using CNNs, RNNs, or Transformers for your projects?
I choose CNNs for image data, RNNs for sequential time-series tasks, and Transformers for language or long-range dependency problems. The choice depends on data type, task complexity, and model efficiency needs.