What is the impact of pruning on model performance?
Pruning reduces the size of a model by removing less important weights, which can improve efficiency and speed.
Does pruning affect the accuracy of the model?
It can, but if done correctly, pruning often maintains or even improves accuracy by reducing overfitting.
What types of pruning techniques are commonly used?
Common techniques include weight pruning, neuron pruning, and structured pruning, each with different impacts.
How do I decide which pruning method to use?
It depends on your model architecture and the specific goals, such as speed, size reduction, or maintaining accuracy.
Can pruning be applied to any type of model?
Yes, pruning can be applied to various models, including neural networks, but effectiveness may vary.
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