Convolutional Neural Networks Explained (CNN Visualized)

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Throughout this deep learning series, we have gone from the origins of the field and how the structure of the artificial neural network was conceived, to working through an intuitive example covering the main aspects and some of the many complexities of deep learning.

Now all these videos have only been focused on one type of neural network, the feed-forward network. The focus of this video then will be to initiate discussion on another very popular and important neural network architecture – the convolutional neural network!

00:00 Intro
00:36 Convolutional Neural Networks Explained

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Soundtrack ➤

♫ 00;00 "Clair de Lune" by RELAYER
♫ 00;37 "Sun" by HOME
♫ 03;04 "Flood" by HOME
♫ 06;20 "Hold" by HOME
♫ 09;39 "Resonance" by HOME
♫ 10;04 "June" by Aire Atlantica

Sources ➤

Producer ➤ Ankur Bargotra

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Комментарии

This is next level explanation

No seriously, so much efforts for this video are clearly seen
1. Visuals
2. Animation
3. Audio
4. Explantion
5. Clarity

really really appreciated ✨✨

Will hit more then a Million views for sure

letmedoit.

Wow, the production value of this video is so high! The explanations are awesome too! Keep going. 💪

AICoffeeBreak

I can not put into words how usefull this video is for visual learners. A big thank you!

Eren-zluw

Awesome video ! I usually watch videos on ytube @ 1.25 or 1.5 speed but this one deserves 0.75 in order to catch all the precious bits of information provided. Great production quality too. Thanks

ilducedimas

Question: at 6:21 if you have 16 filters for the next layer, given the fact that you have 8 inputs after max pooling, then the dimention of the feature maps should be 10*10*(16*6) rather than 10*10*16? How do you combine the outputs of the 16 kernels *6 inputer features to get 10*10*16 features maps?
In other words, when you do the convolutions on the original image, you get 6 feature maps outputs because every kernel is applied to the orignal image. But after maxpooling, you have 6 images and applying 16 kernels on them should results in 6*16 feature maps.

jacobjonm

U are seriously underrated bro. Great content and quality .❤️👍.

ksrikar

This seems like a product of a lot of work. It's quite good, except for the speed. Please consider slowing down, for everyone to fully understand the content.

CurtlyTalks

This is hands down the greatest video I've ever seen explaining neural networks. The way you explain it is so simple and the visuals are astounding! You absolutely knocked it out of the park with this one!

LinkedInGooner

The sheer production effort went into this video blows my mind. The visualization aspect is just too good to be true. Thanks.

raghuramanvenkatesh

one of the best youtube videos ive ever seen, big ups

TheLunkan

I saw the video a second time but at 0.75X speed. way too better. so actually the information provided are decent and well structured, but the speed of presentation along with the noisy cuts make the experience difficult... good work though!

xueupof

WTF channel have I found, this is just like 3b1b but more on computer side, kudos to you man keep it up, you will help a hell lot audience, and more impotantly the resoures you have shared are just👌, Tnx !

im-Anarchy

Dude wtf, this video is absolute gold. I have read books and papers by expert in the field and I have also talked to ML experts and I can confidently say that this video did the absolute best job at breaking down all of these Conv Net concepts! The visuals with the explanation was extremely helpful.

Thank you very much for creating this masterpiece.

newtonpermetersquared

this was really some awwesome level content filled to the brim with knowledge. i always wondered what those mesh like representation actually meant, this was really informative and layman friendly. moreover, i also come to wonder how does those resolution upscalers work, i mean they literally are making pixels and details out of thin air ( and memory maybe, idk its just a asumption on my side), but it will be fun knowing a lil bit more about it.

theencore

I sure am looking forward to the next episode in the series.

Nightscape_

This is one of the best explanations and animations about deep learning!! Congrats for the amazing content!

ju

Thank you for this video! It and others helped me pass my exam! :D

Piccadilly_

One of the only good explanations of machine learning on Youtube, thank you.

RoboticusMusic

How does this not have a x million views, this is unreal

hemorrhagicintelligence

Unless you already understood CNNs, this video will be utterly baffling. Way too fast descriptions without adequate explanations.

wifi-YT

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