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depthwise separable convolution | Complete tensor operations for MobileNet architecture 

When Maths Meet Coding
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This video will give complete understanding about depthwise Separable Convolution , depthwise pointwise convolutions. We will talk about complete tensor operation within it. convolutional neural networks are the backbone of computer vision, and separable convolution are the building block of mobilenetv1 and mobilenetv1 architecture with is very much preferred transfer learning model for mobile devices. after going through this tutorial you will not have any doubt about depthwise and pointwise separable convolutional neural networks.
Here is the research paper link for mobilenetv2
arxiv.org/pdf/1801.04381.pdf

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6 ноя 2020

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Комментарии : 30   
@willyaldaircruzbejar7324
@willyaldaircruzbejar7324 7 месяцев назад
This is the best explanation I've found. Everyone else tends to confuse me when they talk about the operations and dimensions, but here everything is clear. Thank you.
@hasrat17
@hasrat17 Месяц назад
Wooo ... Beautifully explained. Thanks
@huzaifashah2390
@huzaifashah2390 Месяц назад
Watching it too. I have not found such simple explanation
@whataday3910
@whataday3910 3 года назад
I already waited for that theory video. Nice explanation!
@deafxgod6030
@deafxgod6030 Месяц назад
Thank you for your video, great explanation
@mayankupadhyay9288
@mayankupadhyay9288 3 года назад
Informative and helpful video. Great work sir
@aadi7448
@aadi7448 3 месяца назад
Awesome video! Thank you for simplifying things so well.
@Vivek-wn6ii
@Vivek-wn6ii 16 дней назад
clear explanation, thank you so much
@RaghavaIndra
@RaghavaIndra 3 года назад
Super explanation! Keep up the good work.
@anirudhthatipelli8765
@anirudhthatipelli8765 Год назад
Thanks for the clear explanation!
@mikhailgorokhov4197
@mikhailgorokhov4197 2 года назад
Good explanation! Thanks!
@PaulAcademy
@PaulAcademy 3 года назад
Nice Explanation! Kudos.
@rustemc.3850
@rustemc.3850 3 года назад
Awesome explanation!
@vineetkumarmishra2989
@vineetkumarmishra2989 2 года назад
Really great video. Thanks
@saleemabedin6206
@saleemabedin6206 4 месяца назад
Very good explanation. It made the concept clear. Thank You 💙❤
@situ1984
@situ1984 Год назад
Very awesome description
@riorafe
@riorafe 3 года назад
Your explanation is very clear! thankyou
@whenmathsmeetcoding1836
@whenmathsmeetcoding1836 3 года назад
Glad it was helpful!
@adityaghosh8601
@adityaghosh8601 3 года назад
Salute to you.
@senthilkumarponnurangam6800
@senthilkumarponnurangam6800 2 года назад
Nicely explained
@content4553
@content4553 2 года назад
Any suggestion on how can we deal with 1*27 kernel size for convolution on a single image?
@omanshsharma6796
@omanshsharma6796 3 месяца назад
thanks bhai
@tanushsshetty3274
@tanushsshetty3274 3 года назад
Thanks sir
@vikztube7836
@vikztube7836 2 года назад
mate where u plucking 256 from mate, thin air?
@user-jq1yw1ik6q
@user-jq1yw1ik6q 8 дней назад
why 8*8, not 12*12 to find the total number of multiplication ?
@CreatingUtopia
@CreatingUtopia 2 года назад
6:14 to 6:27 is a mystery
@kondurusrikanth9657
@kondurusrikanth9657 3 года назад
nice video dude
@whenmathsmeetcoding1836
@whenmathsmeetcoding1836 3 года назад
Thanks
@aashishchaubeyschannel2676
@aashishchaubeyschannel2676 3 года назад
So many people commenting that was helpful, I have a pen and a paper with me and not able to make sense working it out. Can you please clarify the calculations? For starters to understand the traditional calculations for the convolution, please watch Andrew Ng's ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-KTB_OFoAQcc.html
@ganeshvernekar2797
@ganeshvernekar2797 2 года назад
is kernel is similar to the tensor?
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