Deep Learning(CS7015): Lec 8.11 Dropout

[@rashmidhant3364]

I mean the way sir explained what finally dropout achieves was beyond awesome. Giving out the answer and letting us think why and then putting our thoughts into words. Just Wonderful!

[@adityarajora7219]

Moral of the story: Do not ignore NPTEL.

[@neotodsoltani5902]

Yeah, I was very ignorant toward NPTEL and never watch any of their video. But after this series of lectures, NPTEL is one of my top priorities at learning anything :)

[@anilyarlapalli3866]

Finally my search ended here for the drop out intuition, great video, thanks sir and NPTEL

[@yatrikshah4492]

I have gone through many videos but this one is Best. Finally my search ended here for the dropout.

[@shambhabchaki5408]

Sera! Great!

[@manmeetpatel9475]

Very good. Enjoyable. Please someone take care of volume spike in opening and closing themes :)

[@musasall5740]

EXCELLENT!

[@ernikitamalviya]

Thank you Sir for the fantastic explanation.

[@vaibhavthalanki7320]

10:50 the explanation (for scaling the parameters) i was looking for after watching Andrew's course

[@akshayjain2211]

same here. Infact I got this video as recommendation after Andrew' Ng's video on dropout

[@Dyslexic_Neuron]

But if the probabilities of every node is same then it just scales down the final output . Didn’t know how that is equivalent to passing the test set to all 2^n models and averaging

[@dorgeswati]

in some cases loss increases after adding a dropout . what should be done in such cases? allow more epochs so model can learn more during training?

[@PratikShetty-sv9ig]

Is scaling required during inference?

[@venkateshmunagala8089]

Its a beautiful video

[@morancium]

dude this was awesome