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Fix confusing run-on sentence #2109

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2 changes: 1 addition & 1 deletion intermediate_source/pruning_tutorial.py
Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@
known to use efficient sparse connectivity. Identifying optimal
techniques to compress models by reducing the number of parameters in them is
important in order to reduce memory, battery, and hardware consumption without
sacrificing accuracy, deploy lightweight models on device, and guarantee
sacrificing accuracy. This in turn allows you to deploy lightweight models on device, and guarantee
privacy with private on-device computation. On the research front, pruning is
used to investigate the differences in learning dynamics between
over-parametrized and under-parametrized networks, to study the role of lucky
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