Convolutional Neural Networks (CNNs) explained
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- Опубліковано 1 чер 2024
- CNNs for deep learning
Included in Machine Leaning / Deep Learning for Programmers Playlist:
• Deep Learning Fundamen...
Convolution demo on real data:
• Convolutions in Deep L...
In this video, we explain the concept of convolutional neural networks, how they're used, and how they work on a technical level. We also discuss the details behind convolutional layers and filters.
fast.ai lesson 4:
course17.fast.ai/lessons/lesso...
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Hey! The link to your Machine Learning playlist in the description isn't working.
Thanks, Yousuf! However, when I click on the link, it takes me to the playlist without error. Pasting it below for you just in case: ua-cam.com/play/PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU.html
Additionally, we have corresponding blogs for most videos in this playlist at the link below as well!
deeplizard.com/learn/playlist/PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU
Great video. One small thing I wanted to point out: when convolving a filter throughout an image, generally, it's more accurate to center the filter on the pixel you want to apply on rather than using the topleft pixel of the filter. This probably doesn't matter a whole lot in CNNs, but it does if you are looking to use a specific filter like edge detection, corner detection, etc.
Hello deeplizard, your videos are so good, can you please share with me what application you use to prepare them
great explaination
The excel example was a light bulb moment for me
It was for me too, Sam! I use the same Excel example to illustrate zero padding and max pooling as well, which are tools/techniques commonly implemented in CNNs. If you liked this explanation, then you may be interested in those as well!
Zero Padding: ua-cam.com/video/qSTv_m-KFk0/v-deo.html
Max Pooling: ua-cam.com/video/ZjM_XQa5s6s/v-deo.html
Thank you!!
Credits to fast.ai for the excel example :)
Same here xDDD
Same here - good job!
I'm a data scientist using ML, DL and CNN's in specific on a daily basis. This series is one of the best I've seen. Well done and thank you!
Hi
Can you send me your WhatsApp number to ask you some questions about DL and CNN
wow I wanna be a real data scientist too
not only in my imagination
You just did in less than 10 minutes what my professor could not in 2 hours. I owe you a drink!
One of the best explanations of CNN on UA-cam. Great work.
Great video! I learned a lot from watching it. I just want to point out that at around 5:10, the convolution is not a dot product of two matrices. It's simply A[0,0] * B[0,0] + A[0,1] * B[0,1] + A[0,2]* B[0,2] + ...+ A[2,0]*B[2,0] + A[2,1]*B[2,1] + A[2,2]*B[2,2]. First number in the bracket represents row number, and the second represents coloum number. The dot product of two 3x3 matrices should be another 3x3 matrix.
Hey MakersAll - Thank you! Yes, you're right. What we're doing is summing the element-wise products of each pair of elements in the matrices.
I elaborate more on this in the corresponding blog in the section "a note about the usage of the dot product":
deeplizard.com/learn/video/YRhxdVk_sIs
Thats the comment i was looking for! for a momment I thought I forgot what a dot product was. Glad to see that im not the only one that noticed that mistake.
Guillermo Rodriguez yeah, i was confused a little bit too
Thank you for this comment! I was worried because I thought my understanding of dot products was incorrect.
In addition to what you mention about the dot product, I would like to point out that what is depicted in the video is not a convolution but actually a correlation. It easier to explain a correlation (because it comes down to a simple element-wise multiplication), but the elements are multiplied differently in a convolution (first element of the image patch is multiplied by the last element of the kernel/filter, second element of the image by second last and so on...)
i'm a college cs student who is taking a class on ML (and I have final exam tmrw lmao). this video was such a huge help. I learned more from this video than my professor's entire three day lecture on CNNs thank you!!
Ahnaf Khan haha. It’s weird that college professors usually teach things in the hard ways to follow
Thanks! I've been taking baby steps into machine learning and this has been one of the cleanest and most easily understood introductions to convolutional networks that I've ran into.
I learnt more from this video than from my entire course dealing with neural networks. The visualization is exactly what I was looking for, Thanks!
I had never seen such a concise yet well-explained video on CNN before. This was really helpful. Thank you!
Out of all the youtube Videos on CNN this one nailed it! Great video keep it up , cheers
I've been learning CNN from another course but this made me visually realise what I was learning. Illustration like these leave a lasting impression in mind and help in understanding complex concepts in future. Thank You !
This is my first learning video on neural networks and you made it so easy to understand. And i come from a hardware background, only knowledge that i had around this was digital signal processing.
Kudos to your explaining skills!
THANK YOU. 2 hours of excessive reading and researching and your video finally made me get it!
Great video. The jargon is always the thing that gets on the way for me. Your walkthrough of the visualisation really helped me build intuition about what convolutional layers add to a network.
I like the way you used illustrations to explain the idea behind. It is usually not very easy to imagine all this without the help of visual material. Good work :)
Thanks for the feedback, Eren!
You are Turkish?? 🤢🤮🤮🤮
@@user-yj4qz5lo6k fucking racist..
Thanks for such a clear, to-the-point explaination. I like that you directly pointed out the key behind CNN is nothing but layers of filters. Very well done!
Excellent and clearly explained. Thank you for taking the time to make this video! I've been exceedingly frustrated by other content that doesn't make CNNs as intuitive as you did. Thanks again!
Love how you explain how memory works in convolution. This help understand cognitive learning. Long-term, short-term filter.
THIS IS IT.
Best ever playlist for clearing your prolonging doubts. THE explanations in each video are just so enlightening.
so glad I stumbled upon this playlist. ;)
I watched several videos on CNN but this one is the best video I ever watched on CNN. This video made me understand CNN better than before. Thank you
Have been watching deep learning videos all day since a couple of days, to get the basic knowledge for a big assignment at university (which is due in 4 days :( ). Terribly sleepy, just decided to watch one final video before calling it a night. Decided on this as it was the shortest. Didn't realise it would be this enlightening! Subscribed and look forward to watching your playlists. Thank you!
Thank you!!! This is the best explanation i've found so far.
Mainly because you talk about the mathematics of it with the simplest example. Which is exactly what I was looking for
All videos are great - "succinctly explained".
Please keep sharing more.
Thank you so much.
Great video not only to understand CNNs, but convolution as a concept in particular. Seems like everyone agrees, that Excel example was on point!
The most clearest and simple explanation I have ever seen for neural network and CNN , Thanks
I've been watching explanations on CNNs for over a year now and the part after 4:28 is the best i've seen yet,
The excel visualization of images and filter convolving is fantastic thank you very much for this excellent explanation
Impressive how you managed to answer all the questions I had about CCNs in just one video.
There are plenty of doubts that i had got on this topic. And this video gave me answers for each and every doubt i got.
thank you very much.
Straight to the point & Excellent illustration . Great work!
This was probably the most enlightening video on the subject for me. Your voice is very calming to listen to also :)
I found this after a lecture on CNN's. Now it is much clearer. Thank you
same
I'm doing a thesis about machine learning. This video is a really great help! Thanks a lot
I am so thankful to you for making these videos on deep learning in such easy to understand way
I needed this for my PhD, you're a savior.
You are amazing. This video taught me something in 8 minutes something I've had issues learning for 4 hours. Thank you.
Even if you've written down what you were saying, not stuttering or mincing words is pretty amazing!!!!
The concept has so simplistically and brilliantly been explained. Thanks! 😃
your teaching is better than most paid courses, thank you.
I am new to CNN and this video simplifies it. Thank you for the video.
Simple and clear, best explanation I've watched on UA-cam, thank you
That was a very good introduction to the basics of CNN, thanks
Amazing. Explaining such a complex concept in such a concise and clear presentation, just amazing. Thank you.
A great visual explanation, thank you ! It really helps understanding the concept
One of the best explanations of CNN on UA-cam. Great work.
The excel spreadsheet was the key to understanding CNN for me.
Thank You ! It must have been really hard to put the video together but let me tell you, you just did a great job. Thank you !
Great video. Working on advanced projects regarding rcnn. The basics from this video really helped cover weak points!
Can't thank enough for this simple and beautiful visual explanation
I keep finding deeplizard gems like this one in your playlists. Thanks.
These videos are very helpful I was trying to wrap my brain around this but this video made it so much easier. Definitely subscribed.
I really like the way you explain. No matter if you're new to this topic or just refreshing from a different POV. Subscribed and thanks!
This was one of the best explanations on Conv Nets outs there! Thanks!
You explained filters like no one did.. Thank you very much
Brilliant! If you can describe this to someone like me to even vaguely understand, that is brilliant. Superb teaching.
Thank you very much for this video! I am very new to CNNs, and watching this video helps me understand how CNNs work in general!
5 years later and this video is still awesome ♥
Simple visual overview of CNN! Great job!
Highly underrated. Great Video. Liked it before the video even ended!
I'm a software developer and heard about CNN for a while, but didn't know how it works. Right now I kinda understand it. Computer science fascinated me again. Hooray for we human.
Liked it. Thanks for brief explanation of CNN. I was worried that it is much more complex but you made my life simpler
I have seen many explanations -- yours is the best yet -- thank you
mike
I feel moved to say some big thing to praise your efforts, but the reality is that I wouldn't be doing computer science if I had a gift for speaking. Big kudos, this video is exactly what I needed to understand convolution.
The best explanation of CNN I've ever seen. Thanks a lot!
great explanation.......I was confused about CNNs but this video cleared my doubt.......thanks deeplizard
This is BY FAR the best explanation ever given for CNN and makes sense. Thank You.
For other explainers: "If you can't explain it simply, you don't understand it well enough." :P
Thanks so much for the video! It was always a struggle to understand CNN (cuz ppl explain CNN in a most complicated way😢) but your video really helped in understanding the core of CNN. Keep up the good work 🤗
Usually people say that Convolutional Neural Networks are the hardest of all types of neural networks to learn to use, but you made the basic idea of it clear as the sky.
Tbh you won't know till you try. People say the kanji in Japanese is really hard to learn, but when you try it out in ain't so bad. I guess it depends on the individual to whether it is hard or easy.
very well explained, currently preparing my thesis on skin analysis. CNN greatly helping out
Excellent visualizations to explain exactly what's happening.
This deep learning playlist is extremely cool! Thanks!
Previously watched another video on CNNs - which was way longer - and by the end, I had no idea. In just 8 mins of your video, it was like a light bulb moment. Fantastic work.
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best explanation for CNN. Thank DeepLizard for great work!
That excel example though, what an excellent way of teaching!!!
Best explanation about Convolutional Networks i ve ever seen. Awesome 👍👍👍👍
Great! Thanks a lot! I enjoyed the view on how to define the filters for edges! Saw the light! I will be following your next updates!
That excel example is genius way to explain things!
AMAZING EXPLANATION!!!! Very well done!!!! I am super impressed!!!
I understood the concept very clearly...Thank u so much for this video...
You explain things so clearly! Thank you!
This was such a great video and was exactly the introduction I needed. Thank you!
I like the way you explained it. Very clear and well prepared. Thank you :)
Thorough and to the point, love it
This is such a great tutorial. Very well explained. Thank you so much.
Thanks a lot,
It really helped me a lot to get the feel of Convolution Neural Network which i wasn't able to get for quite a long time.
Keep up the Good Work .
Nice! enlightening explanation, loved the illustrations, makes everything brighter to understand
this is the one of the best video to explain CNN n the filter examples are great. thanks
Thank you so much for the video. So simple and easy to understand, a really good and solid step into learning about this. I feel more enthusiastic right know to continue my personal development on this research part.
OMG!! The concept was very well explained. Thank you
Very nicely explained high-level overview of CNNs.
Excellent explanation! So glad for all the free learning content!
Love this video! Very clear explanation, thank you!
Nice visualization, adequate explanation of complex topic and pleasant voice. I am sticking here.
Man, what a way to explain :) You should do this with more Neural Network topics.
Great video!!! Very good tutor too, helped me understand straightforward the CNN's.
Very helpful. The examples used for explanation made things much clearer.
Amazing video. Really one of the best I have seen about the topic till now.
The notion of a feature or feature extractor from classic machine learning, for instance in pixel-wise image classification using random forrest or XGBoost, can help to understand how a bunch of (initial) random weigths (filters) can be learned (more like taught) in order to produce the right output (prediction). People jump right to Deep Learning and forget about what came before (before back propagation as a learning method).
Very clear and well done. Thanks for making this.
Amazing. Helped to comprehend CNN better.
Nice explanation! got a good grasp in just 8 minutes.