What is a common strategy to manage the computational complexity of a deep learning model without significantly compromising its predictive power?
- Increasing the model's depth without regularization.
- Pruning redundant neurons and connections.
- Reducing the batch size during training.
- Using a shallow architecture.
One common strategy to manage computational complexity in deep learning models is pruning redundant neurons and connections. This involves removing unnecessary parts of the model, which can reduce computational requirements while preserving predictive power.
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