Merge branch 'master' into DQN_revise_training

Author: SiftingSands

intermediate_source/pruning_tutorial.py

@@ -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