Pruning refers to a machine learning model optimization technique that consists of selectively eliminating some of the connections and/or neurons of a neural network to reduce its complexity and improve its efficiency.
Pruning can be performed during the training phase or after the model has been trained. During training, pruning can be applied to prevent the model from overfitting or to speed up the training process. After training, pruning can be applied to reduce the size of the model and make it easier to implement and run on resource-constrained devices.
There are several pruning techniques, including the removal of neurons and connections with minor importance, the removal of neurons and connections according to their relative importance, and the removal of neurons and connections according to their activity during training.
Pruning is an effective optimization technique to reduce the complexity of machine learning models without sacrificing their accuracy. It can improve model efficiency, reduce storage costs, and accelerate model execution speed. However, it can also be a complex process and may require careful tuning of model hyperparameters to achieve the best results.
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