Pruning is a machine learning optimization technique which is applicable to deep neural networks (DNNs). Pruning involves finding neurons (often called weights) in neural networks that do not contribute significantly to the performance of the model, and then removing them. This can improve the processing speed of the model, so long as it is done in a way which can be accelerated by the underlying hardware. It typically results in reduced model size, improved efficiency, better generalization and increased interpretability [1].

Image credits: T. Liang et al,

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