Convolutional Neural Networks Explained (CNN Visualized)
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- เผยแพร่เมื่อ 27 มิ.ย. 2024
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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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Sources ➤
[1] • CNN: Convolutional Neu... (Neural Network That Changes Everything - Computerphile)
[2] • Inside a Neural Networ... (Inside A Neural Network - Computerphile)
[3] • Convolutional Neural N... (Convolutional Neural Networks [CNNs] Explained - Deeplizard)
[4]ujjwalkarn.me/2016/08/11/intu...
[5]towardsdatascience.com/intuit...
[6]towardsdatascience.com/gentle...
[7]towardsdatascience.com/types-...
[8] • Recurrent Neural Netwo... (Recurrent Neural Networks [RNN] & Long Short-Term Memory [LSTM] - Brandon Rohrer)
[9]towardsdatascience.com/the-mo...
[10] • Variational Autoencoders (Variational Autoencoders - Arxiv Insights)
[11]www.cs.cmu.edu/~aharley/
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talk about future of computing make a 2 hour video
how bio cpu or quantum cpu can change the world
talk about the future of pc, cpu and light speed cpu and more
the link for Interactive Number Recognizer is dead :(
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
I almost agree with the comment, except with the prediction on the 1 million views. You need to be more specific with this objective, (or S.M.A.R.T.?) One year later is far from 1 million. And it's an amazing explanation.
Wow, the production value of this video is so high! The explanations are awesome too! Keep going. 💪
The sheer production effort went into this video blows my mind. The visualization aspect is just too good to be true. Thanks.
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!
One of the only good explanations of machine learning on TH-cam, thank you.
This is one of the best explanations and animations about deep learning!! Congrats for the amazing content!
Amazing explanation, brilliant production quality and sleek animations. Hands down, one of the best places to get a high level view on machine learning topics available on YT. Thanks mate for the effort.
Well, I've watched 4 videos to understand CNN, and I can say this is the shortest and clearest one. Thanks, man!
The visualization is simply phenomenal. Amazing job!
You explained so much in such less time in such simple words. Huge thanks!
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.
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
This is amazingly visualised and explained. Visualization always really helps to understand the real pictures of the ideas, especially for beginners.
Wow! This video is so great! Rarely do I see such a clear visualization of the topic!
visualizing it makes so much easier to understand. Thank you
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.
dude this video is ultra high quality. you are criminally under sub
Brilliant explanation with Incredible animations. Really sutisfying to watch, when you see the process and understand it.
Thankyou for the brilliant explanation with the thoughtful graphics.
beautiful explanation with visualization - easy to understand
THE PRODUCTION QUALITY. The ratio of it with the views and subscribers is WAY off. This deserves views in millions. Not to mention the way these complex concepts were explained, this is the best video I have ever seen for the explanation of CNNs. Hats off.
I just want to add to what folks are generally saying: hands down one of the best videos about CNN's on TH-cam
I can not put into words how usefull this video is for visual learners. A big thank you!
Really needed this visualization to actually understand weeks' worth of university lectures...
This was extremely well done
U are seriously underrated bro. Great content and quality .❤️👍.
Jonkeen has a channel u should look up some of his older videos
@@rebeccarpwebb4132 name of the channel?
@@ksrikar6668 jonkeen and bestdamnpodcast.... Lots of videos . this video showed up under his . i find really good channels from his
Its small lil channel no commercials. This guy is just consumed with his research and i find it fascinating and lots of other good stuff to look up
I dont know how this content is free but thank you so so much!
Congrats for the animation dude! One of the best visualizations I have seen on the topic. The vaporwave music was also a nice touch. By the way, which software do you use to animate this?
🙌.
Great Watch looking forward for next update
my friend..
Best Explaination i found wow, keep it up, so easy to understand thank you very much i got a exam about that tomorrow!
excellent video. Just one thing, as far as i know if you convolve an input image with 3 channels and a filter with the same number of channels, you end up with a feature map of one dimension instead of 3. Convolution happens for each channel between the input image and the filter and then you sum up the values between channels at every windowing step
U got yourself a new subscriber.
I hope this channel blows up very fast.
really nice work mate!
Awesome explaination sir, thank you
Really good explanation!
Hey Futurology, You saved my A** ...Love from Ethiopia!
That's damn awesome. the visualizations are badly awesome
THIS IS SO GOOD!!
insane job bro !!
I sure am looking forward to the next episode in the series.
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.
Thanks for watching! Upscalers typically use autoencoders (inverse graphics networks), we do plan on making videos on these networks and their applications in the future!
@@OptimisticFuturology that's just great, and you're welcome.
After watching bunch of videos this just clicked and everything just clicked, thank you for this wonderful video.
amazing video and amazing visualization
This was a great explanation. Thank you. Now I feel like I can actually understand some other videos which dive a little deeper.
Amazing video! well-expanded and visually captivating 👏
10 minutes of pure bliss!
Man, your work is Phenomenal!!! Thanks💯
Great video! Way more helpful then another online course I am taking from Carnegie-Mellon!
That link to the interactive digit recognizer is dead... Has that been updated or is it just not available? Thanks!
the best video for CNN i could ever find, seriously
Incredible explanation. Love your way how you work
This explanation was outstanding!!!
Mind blown 🤯. Love this explanation. i am subscribing just cause of this video. This is the the kind of fast and easy to understand video i was looking for
Wow, the intuitive explanation and great production quality of this video makes this one of my favourites that I have watched on this topic 🎉
one of the best youtube videos ive ever seen, big ups
Great video!
Great content, quick question, can we specify a specific edge detector to be used for the kernels? or does the convolutional layer by default has one? if so, what's the point of having multiple filters?
I wonder how such calculations could have been carried out the first time when the computers weren't so advanced. The pioneers of AI are such brilliant people 🤝
Great video, thank you so much, your efforts are highly appreciated!
Thank you very much!!!!
Great video, thanks so much!
this explanation is overpowered
i love the BGM so much❤️❤️
What an ABSOLUTE BANGER! Shukran Habibi
Danke!
great !!
my brain is exploding but in a good way, thanks for this!
Spectacular video!
The visuals were dope!
Thank you for this video! It and others helped me pass my exam! :D
It looked like a holly wood movie.. Great explanation.. I totally liked it..
great demo thank u so much
Thank sir making video you are doing great job
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.
best video!
What a great video! Great production too! Let's get iiiit!
This video needs to be appreciated 🎉❤
Great explanation!
Thanks for the recommendation on brilliant!
THANK YOU SO MUCH
Great 🔥
Next level explanation
Really thanks
How does CNN become rotation and orientation invarient? Can this be understood with a visualization using few images that rotation/re-orientated and then their output followed through the layers and architecture of CNN ?
How do you connect 400 pixels of high-level features from last pooling layer to the input of 120 tensors of Classifier network ?
Amazing man......
07:28, how did the feature maps count jump from 6 in Pool1 to 16 in Conv2 ?
i tried accessing the adam harley page but it was showing tht i am not allowed to access the page..where else can i access that resource
I love interactive tools like that number recognizer. Do you know of similar ones for more cnn's and/or other architectures? Text, image, any modality.
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!
if you created a course on this topic, i'd pay for it
thank you
Can someone explain the dimensionality of going from the Pool1 to Conv2 layer? I end up in 4D space.
Hey i am looking to make animations for VIT. What did you guys use to make the animations
I cant access the interactive website but other than that this was really good
Great video but the second convolution layer is poorly explained. If you have 16 kernels, are those applied to each of the 6 previous images? Then you counting of pixels are wrong but if not how do you produce those 16 5x5 images?
How to define initial number in the filter?
these visuals are insane ??
Please make a vidio on predictions crack, joints, corrosion in vidio of any mechecnical part using CNN and also make rectangle box around any fault like crack.
Dear uploader, please consider to remove ads in the middle of the video, they are highly distracting. We are here to learn, not for trivial things. Ads at the beginning and at the end are more than enough. Thank you.