C4W1L04 Padding

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C4W1L04 Padding 0:09:50

C4W1L04 Padding

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Padding and strides 0:14:55

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Комментарии
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Andew is one of the most knowledgeable person on machine learning out there.
His explanation is much based on theory. Thank you very much sharing the valuable info in youtube

kvishnudev
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Such great explanations to what's going on under the hood. Knowing is one thing, teaching is an entirely different animal. Kudos to the teacher for having mastered both!

seanmenzies
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Was struggling with some conceptions and this lecture provided insights I needed. Thank you so much!

shahriarrahman
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When the stride is greater than 1 and we are using SAME padding concept, thus the size of the input image is retained.
For example : input = 16, filter = 5, stride =5. Is value of P =(5-1)/2=2 ? Thus makeing the output of size [(n+2p-f)/stride]+1= 4
It can be seen clearly that output size is not retained.

vipinmakde
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Thanks for your great videos, but please can you explain which parmeter(s) determine the value for each member of the filters, I mean why do you use (1, 1, 1 / 0, 0, 0 / -1, -1, -1) but not other numbers.

Thanks advance.

aramroshani
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An extremely insightful lecture as always have been. But when to use Valid padding and when to use Same it wasn't clear, I mean in which scenarios we'll consider them and why?

rahuldey
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Thank you so much for the explanation on zero padding. Why do we use 0 for padding and not 1 ?

anjanakesavan
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What is the difference between reflective padding and zero padding ?

Shewanee
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***Note:
5:58 - Valid Convolution actually means that the input image size is 'valid' for this operation and there's no need for padding...

rohanshetty