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intermediate_source/pruning_tutorial.py
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known to use efficient sparse connectivity. Identifying optimal
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techniques to compress models by reducing the number of parameters in them is
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important in order to reduce memory, battery, and hardware consumption without
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-sacrificing accuracy, deploy lightweight models on device, and guarantee
+sacrificing accuracy. This in turn allows you to deploy lightweight models on device, and guarantee
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privacy with private on-device computation. On the research front, pruning is
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used to investigate the differences in learning dynamics between
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over-parametrized and under-parametrized networks, to study the role of lucky
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