Tutorial 24- Max Pooling Layer In CNN

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  1. Krish Naik
  2. coursera
  3. Maxpooling
  4. great learning
  5. krish
  6. appliedaicourse
Рекомендации по теме
Tutorial 24- Max 0:06:37

Tutorial 24- Max Pooling Layer In CNN

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What does the 0:00:34

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Pooling layer 0:05:10

Pooling layer

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CNNs Pooling layers

Комментарии
Автор

I don't know who came up with this Max Pooling but he must be a genius. Thank you for the video!

gxhkgtk
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Really so simply explained and now see the difference how a upgrad professor explained the same concept -

Max pooling: If any one of the patches says something strongly about the presence of a certain feature, then the pooling layer counts that feature as 'detected'.
Average pooling: If one patch says something very firmly but the other ones disagree, the pooling layer takes the average to find out.

mukund
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whenever i have doubts... i visithere...go back with good knowledge

Tales.of.Irshad
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Awesome explanation & thank you.Highly inefficient channels like *DONT WANT TO TAKE THE NAME* takes thousands of rupees and teaches with about 10% proficiency as you do. This will take me to a step closer to my paper. :)

koushikshomchoudhury
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This guy is a legend of the game I was watching 7 hours of deep learning video in which CNN WAS 1 HOUR AND my doubts were still not cleared this guy did it in few minutes I am highly impressed by your skills Sir

Gester
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Superb video.Read a lot and saw videos of maxpooling but this one cleared all my doubts.Thanks Krish. Keep it up.Cheers.

sandipansarkar
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sir we need remaining theory part and coding part of CNN, please...

harshays
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In no existing framework anyway does max pooling round up the output dimension size. If stride takes you off the edge, you don't include it. The output dimension for a 3x3 image, with a kernel size of 2, and stride of 2, is 1x1

EssDubz
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Hey Krish, Can you please explain about the strides and How to set up the values for strides in tensorflow ? Thanks

harendrakumar
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Thank you, Krish Sir. Nice tutorial on max pooling.

vishaljhaveri
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As per your previous video, you informed that if padding layer is added then the formula is n-2p-f+1. Hence if we apply the same here with P=1, then we should get 1X1 matrix rather than 3X3. Correct me if I am wrong.

vermaanky
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Am We do padding so that dimension will not reduce then we do max pooling that reduces the Though I understood the very purpose of max pooling but this dimension reduction process making me confused

aditisrivastava
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Please provide the research paper link

mdmynuddin
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KRISH>ANDREW NG LOVE FROM PAKISTAN!

alirizvi
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Hi Krish,
Please let me know, in what scenario we should use average pooling over Max pooling.

shaz-z
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thank you so much for this explanation, can you please provide the formula of the Max-pooling

loaialamro
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Hi Krish, please continue your deep learning series.

meghachoudhary
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Hi sir, really awesome explanation, but just one question did someone hit you before creating this video? I can see injury marks on your face.

pankajyadav-entb
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I think that the filter dimension is a hyperparameter that is fixed and cannot be updated during backpropagation. Still not sure, correct me if I'm wrong.

aakankshajaiswal
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Hi krish. I am confused about one thing. Once we have applied the filter on the image, does it pass through the activation function and then go to the maxpooling layer or the activation function is applied twice ?

rithwikchhugani