Can memory optimization reduce overfitting?

Can memory optimization techniques help in reducing overfitting in machine learning models?

Yes, memory optimization can help reduce overfitting by allowing models to generalize better through efficient resource management.

What specific memory optimization techniques are effective for this?

Techniques like pruning, quantization, and using smaller batch sizes can help optimize memory and reduce overfitting.

How does pruning specifically contribute to reducing overfitting?

Pruning removes unnecessary weights from the model, simplifying it and helping to prevent it from fitting noise in the training data.

Are there any trade-offs when using these optimization techniques?

Yes, while they can reduce overfitting, they may also lead to a loss in model accuracy if not applied carefully.