Answers for "dropout regularization"

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dropout regularization

It works by randomly "dropping out" unit activations in a network for a single 
gradient step. The more you drop out, the stronger the regularization:

1-) 0.0 = No dropout regularization.
2-) 1.0 = Drop out everything. The model learns nothing.
3-) Values between 0.0 and 1.0 = More useful.

Posted by: Guest on May-07-2021

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