Why might one choose to use a deeper neural network architecture over a shallower one, given the increased computational requirements?
- Deeper networks can learn more abstract features and improve model performance
- Shallow networks are more computationally efficient
- Deeper networks require fewer training examples
- Deeper networks are less prone to overfitting
Deeper networks can capture complex relationships in the data, potentially leading to better performance. Despite increased computation, they may not always require significantly more training data.
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