What is Dropout Regularization | How is it different?

Overfitting is one of the main problems we face when building neural networks. Before jumping into trying out fixes for over or underfitting, it is important to understand what it means, why it happens and what problems it causes for our neural networks. In this video, we will look into dropout regularization. We will learn how this regularization technique works, how it is different than L1/L2 regularization and when to use it.
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[@rr633]

best explaination why we multiply by 1/p

[@mehdismaeili3743]

Excellent, thanks.

[@misraturp]

You're very welcome :)

[@shri9229]

Bill gates is a good example

[@ax5344]

i was watching Andrew Ng's lecture: ua-cam.com/video/D8PJAL-MZv8/v-deo.html Somehow I feel he just glossed over the concept. I just heard inverted dropout is very good but got lost by the why. I replayed, still cannot get it. Thanks for your video. It is short and to the point.

[@londonl.5892]

This is a great description of dropout regularization! Is there another video on why this technique stops overfitting?

[@misraturp]

Thank you! I am talking about why regularization works on overfitting on day 14: ua-cam.com/video/h095wobw9xE/v-deo.html

[@Camaron242]

Thank you

[@bay-bicerdover]

Fevkalâde!