Depthwise Separable Convolution - A FASTER CONVOLUTION!

[@IndiaNirvana]

Very crisp explanation loved it.

[@masoudmasoumimoghaddam3832]

I love the videos you make. The good thing is that you explain the concept right to the point and don't waste time which means you are dominant on the subject. I really hope you don't lose the motivation to make such tutorials. because there are enthusiasts like me and my colleagues that literally waiting for your future videos. So please keep making videos

[@sarthaknarayan2159]

Your channel is underrated and is pure gold

[@王大哥-e1c]

thanks for your high-quality videos which really help me a lot

[@VikasSingh8]

love what you are doing, your recent videos were really helpful to me, keep up the good work, keep exploring and uploading videos 👍

[@CodeEmporium]

Thanks! Glad you like the videos!

[@GunnuBhaiya]

This is a wonderful tutorial which deserves (and in the future will get) way more views

[@CodeEmporium]

Rahul Gore Hoping the same. Thanks!

[@deepaksingh5607]

You explained it in the best way.

[@maxsch.2367]

absolute banger! well done

[@ParniaSh]

Well explained, beautifully demonstrated. Thanks!

[@7688abinash]

That put down so simply. Just loved it :) Thanks a lot

[@CodeEmporium]

Abinash Ankit Raut glad you liked it! Thanks!

[@ShahriarMohammadShoyeb]

Brilliant explanation, described in a very understandable way.

[@CodeEmporium]

Shahriar Mohammad Shoyeb thanks! Glad you liked it !

[@myhofficiel4612]

well explained , you made it look really easy !

[@esrabetulelhusseini2330]

provide a clear understanding to me .so glad,thank you

[@starcraftpain]

Finally understood MobileNets and DSCs. Thanks the for clear video!

[@mamuncse068]

Excellent video,easy and well explain of Depth wise Separable Convolution.Really grateful to you.

[@张程-f6d]

really helpful for me to understand the depthwise separable convolution! Thank you!

[@vamsiKRISHNA-io1yi]

Simply Brilliant thank you for much for a detailed information about Xception

[@jeamsdere9636]

This video is real helpful. thank you

[@yungwoo729]

Perfect explanation. I appreciate it. Thank you!

[@Kumar731995]

Awesome video, subscribed! Had a really good understanding of what depth wise separable convolution is at the end of the video.

[@sami-h5y]

That was a very lucid explanation, thanks.

[@CodeEmporium]

Glad you found it usefule Sangeet

[@austinmw89]

Best explanation I've found on this, thanks

[@CodeEmporium]

As long as it helps :)

[@keithchua1723]

Imbeccable explanations as always!

[@senli2229]

Great help for understanding DepthWise Seqarable Convolution!!!

[@Vinay1272]

Thanks a lot for this! Very helpful.

[@thecurious926]

omg this came out 4 years ago? I am living under a rock

[@AmartyaMandal7]

Amazing Explanation!

[@Frostbyte-Game-Studio]

this is fantastic explaination

[@CodeEmporium]

Thank you so much!!

[@zhuotunzhu8660]

Very clear, make it easy to understand! Thanks!

[@CodeEmporium]

Zhuotun Zhu anytime! Thanks for watching

[@maheshwaranumapathy]

Great video, reading the reference paper is going to be much easier now

[@mayankchaurasia4483]

Awesome explanation . Loved it.

[@CodeEmporium]

Mayank Chaurasia So glad you loved it :)

[@huythai6210]

It is so useful and clear

[@vinitakumari5913]

Explained it so simply. Thanx

[@CodeEmporium]

No worries. Glad it helps!

[@duongkstn]

incredible !

[@bearflamewind]

Thank you so much for making such a nice video that is so easy to understand.

[@CodeEmporium]

GUO GUANHUA For Sure! I'm glad you understood it :)

[@win-n6k]

great video,looking forward more

[@Ganitadava]

Super explanation

[@PalashKarmore]

Thank you. You saved me a lot of time.

[@CodeEmporium]

It's what it do. Thanks for watching :)

[@felippewick]

Great video. Helped a lot!

[@dufrewu7437]

very helpful video, thanks

[@srinathkumar1452]

Nice video! I look forward to future videos on object detection and semantic segmentation.

[@roberttlange8607]

Great explanation! Thank you very much!

[@knowhowww]

Okay, now I get it.... Thanks!

[@RafiqulIslam-je4zy]

Many many thanks.

[@RishabhGoyal]

Very clear explanation.. Thanks a lot.

[@CodeEmporium]

Welcome! Glad you got some use out of it

[@RishabhGoyal]

CodeEmporium Yeah.. I was reading W-Net where they have used it..

[@harv609]

Amazing .. explained so clearly !! Thank you

[@CodeEmporium]

Harsha Vardhana anytime! Glad you liked it!

[@virajwadhwa6782]

Is standard convolution here and depth-wise separable convolution functionally equivalent? That is, they will both give the same outputs for a certain input? It is just that, depth-wise separable convolution saves on computations, but is otherwise functionally the same right?

[@Maciek17PL]

What would pointwise convolution look like in a 1d separeble convolution???

[@harutyunyansaten]

thank you, understood

[@sahibsingh1563]

Awesome explanation

[@lonewolf2547]

Awesome video dude

[@yx9873]

Well. I can't understand why the input size of the second phase is still M. Is that a typo?

[@nexushotaru]

Thank you for explanation, but please, use more intuitive designations (like H for height and W for width)

[@gaussian3750]

Thanks for explanation

[@sergeyi2518]

Is it correct that arbitrary standard convolition cannot be exposed as depthwise convolution (except some special cases)? Depthwise convolution is just another type of convolution, right?

[@willz3222]

This is excellent

[@wuxb09]

Good Explanation! Thanks

[@reactorscience]

Amazing video sir.

[@66tuananh88]

Do you have a python code 3d depthwise separable convolution?

[@sourishsarkar5281]

Why are the output number of features always an integral multiple of the number of input channels?

[@Lucas7Martins]

Loved it!

[@bhuvneshkumar1970]

2:00 shouldn't it be (Dk^3 ) * M? As matrix multiplication of size (n x m) . (m x p), no. of multiplication are n x m x p.

[@UniversalRankingOfficial]

Can you make a video on Resnet Architecture for beginners?

[@sounakbhowmik2841]

thank you, it was of great help !!

[@vaibhavsingh1049]

This was great.

[@gopsda]

Great! Neatly put. Thanks for the video. One thought -- we can add one parameter lambda as multiplication factor in the combination step, and treat as a trainable parameter which increases total trainable parameters by 1 but may help converge the solution faster, I guess. Depthwise sep conv = Depthwise conv + lambda * Pointwise conv.

[@strongsyedaa7378]

Where to use depthwise separable convolution? How do we come to know to where to use it? 🤔

[@gopsda]

@@strongsyedaa7378 Wherever you want to reduce number of trainable parameters. Most of the networks are defined with this depthwise conv.

[@ducpham9991]

very clear!

[@busy_beaver]

Thanks!

[@zhengxiangyan3654]

excellent！very nice video

[@meyouanddata9338]

amazing content. thanks alot :)

[@artsyfadwa]

Nice video. Thanks.

[@joruPT]

This video was very helpful, thank you :)

[@CodeEmporium]

Welcome. Glad it was useful!

[@judyhwang9342]

excellent

[@CodeEmporium]

Thanks!

[@strongsyedaa7378]

Where to use depthwise separable convolution?

[@MasayoMusic]

Thank you for this. What are you using for animations?

[@jo-of-joey]

omg. you just saved the day!

[@CodeEmporium]

You can always count on your friendly neighborhood data scientist..

[@jo-of-joey]

can you do a video on Binarized Neural Networks?

[@melihaslan9509]

very nice!

[@kartikpodugu]

easy to understand. i suggest to add animations for better understanding if possible. thanks

[@ah-rdk]

Thanks.

[@abhishekchaudhary6975]

Thanks

[@digvijayyadav3633]

worth the time!!

[@varchitalalwani3802]

very helpful, thanks

[@CodeEmporium]

Glad it was helpful. Thanks for watching!

[@rabhinav]

Hey really helpful Thank You. Can you also make a video on Winograd Convolution?

[@ranam]

Ok genius iam also approaching the problem same way like you I don't use matheMatical way my question is so simple because LTI depends on convulution here's my question below Convulution is nothing but stacks and scales the input that's why the input to an amplifier is stacked and scaled or amplified but in filter design it attenuate frequency so I don't know how it regret certain frequency by stacking and scaling the input if possible some one explain to me

[@willemprins4564]

How does this do with Res and Densenets?

[@harshnigm8759]

In the Xception research paper they actually used skip connections and dense layers , skip connection were reported to have given a major boost to the final accuracy.

[@mohitpilkhan7003]

"immediately" hahaha. Thanks bro. SUbscribed

[@aq555]

good

[@harshadj13]

Sakkath video!

[@rahuldeora5815]

Hey, I am making a video using some of your animations. Hope its cool!? It's on MobileNets

[@CodeEmporium]

bluesky314 Absolutely. Just list this video in your references. Send a link to your video here when you're done. I'd like to see it :)

[@rahuldeora5815]

Thanks! Here it is: kzbin.info/www/bejne/fnWcd6Ggi9yIbsk Would love your feedback

[@rahuldeora5815]

hey

[@mathematicalninja2756]

Tjis is like mapreduce

[@DarkLordAli95]

First, thank you for making this helpful video. Second, why can't comp sci people agree on one notation for anything at all?! It's like for every video I watch I gotta learn a new set of notations... BOY. And why is F the input and not the filters? that's just straight up confusing man. humans really can't agree on anything.

[@pawansj7881]

Good1

[@Jonas-qz2gb]

Thank you so much for this amazing explanation!

[@santhoshkolloju]

Very helpful Thank you

[@CodeEmporium]

Thanks! Glad it was of use.