Network pruning is a model compression technique that removes redundant or less important weights, connections, or entire neurons from a trained neural network to reduce its size and computational requirements.
Pruning a neural network is like trimming a tree - you remove the branches (weights) that don't contribute much to the tree's health, making it more efficient while maintaining its core function.
Benefits of Pruning
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Model Size Reduction
Significantly smaller file sizes for storage and transfer