That's a masterpiece, not only have I learned how in detail convolutional neural networks work, but also I've learned how I should explain hard subjects to others. Thank you.
I tend to get intimidated by videos longer than an hour, but I'm so incredibly glad I watched this one! Super clear explanation, I feel like I actually understand what happens now. No one else has been able to explain it so clearly. :) Thank you!!
@@BrandonRohrer a little suggestion it would have been a lot better if it was a playlist consisting of 10 mins videos each, it would really be helpful for someone with low attention span like me
Although it's 5 years ago, this is the simplest and the AWESOMEST video in youtube for someone getting started with Computer Vision. This lecture, along with 3-Blue 1-Brown neural network playlist, and you are good to explore Thank you!!
Don't let the duration of this video intimidate you from enjoying this masterpiece of a presentation, just press play and begin, you'll freaking love every second of it. Thank you so much for sharing this and so much other information for free!
One of the best videos I’ve ever seen on the topic: super clear explanation + truly in depth, all without being boring. The only thing I didn’t understand is how to determine the values in the matrices for the convolution.
Thank you Daniele! The short answer: they start random and get adjusted during training by backpropagation ( e2eml.school/backpropagation ) The long answer: A two-course sequence walks through how to implement this in Python for 1-D ( e2eml.school/321 ) and 2-D ( e2eml.school/322 ) convolutional neural networks.
Thanks so much for spending time preparing this videos. Watching is 1h, preparing for this video is probably * by 100 :) 3:20 - Filtering 8:10 - Pooling 10:30 - Normalisation (ReLu) 12:16 deep stacking 13:11 fully connected layers 17:00 receptive fields 18:00 create a neuron, create weight and squash the results (sigmoid function). 26:50 optimisation
I had a diffi ultrasound time understanding the convolution layer, this course is the best among all courses I saw on TH-cam, keep the good work, you saved me , I was struggling understanding and now I'm completely clear. Thanks alot
I can't stress enough how great your videos and explanations are. I get overwhelmed by lots of text and missing visual examples, so it's great I found your videos. Watched 2 already and will definitely watch the rest too!
Brandon, you explain the most difficult concepts in simple understandable language. Nice visualizations create a mind map which we cannot forget. Thank you for all your efforts on these videos!
This is by far the best explanation in convolution neural networks, gets into theory and details of things. The presentation of everything is superb. I now know precisely what CNN are exactly all about. I would never spend a full hour watching an explanation on youtube unless it is a full course. This explanation hour long of CNN is well worth it. Thanks.
This is the first video of yours I have watched. It was so good that I subscribed to your channel. BYW, your voice is a lot like Brian Greene. This is good because it is a good lecture and documentary voice.
Wow, i this tutorial is packed with information. I had to rewind a 100times to grasp the art about weights & errors, nobody ever explains this part for mere mortals like myself.
Marvelous explanation, made simple and concise, yet not oversimplified to a level that would render it pointless. I could not have imagined a better way to bring the loose pieces in my head together. Thanks a lot for this.
In this simplified example yes, but just a heads up that in practice it's often done just like the other layers - summing up all the inputs and passing them through an activation function, such as the logistic function.
Sir, just finished watching and you explained this very well especially the second half with gradient descent and backpropagation. Thank you so much, have liked and Subscribed!
Thank you for this amazing video! It definitely helped clear a lot of stuff about CNNs for me. On a very random note, you have a great voice! I feel like you'd make an awesome audiobook narrator!
Thanks! If you want to go one level deeper, I recommend walking through e2eml.school/321 and e2eml.school/322 . They walk through the Python implementation and give a deeper understanding of how and why.
Thanks! Here's a bit more on convolution that might help clarify: th-cam.com/video/B-M5q51U8SM/w-d-xo.html And if you want to go really deep , there are courses here: end-to-end-machine-learning.teachable.com/p/321-convolutional-neural-networks and here: end-to-end-machine-learning.teachable.com/p/322-convolutional-neural-networks-in-two-dimensions/
one question @57:43 Convolutional Neuronal Networks can also be applied to 1D data for example audio right? It isnt necessary to convert the data to look like an image?! or i dont get it right. I mean if i have an array [1,2,3,4,5,6,7,8] i can apply 1D Convolutions on it .
You are right! 1D convolutions are not as common, but totally a thing. I'm just completing a course on building one from scratch in python to categorize electrocardiograms: e2eml.school/321 .
its clear till 17:57 , but i just lost it at 18:01, just didnt understand why each lines there changed from 1.0 to -0.2 , 0.0 , 0.8 , -0.5.......can someone explain ?
It would have been nice to see the in-depth breakdown of convolution layers instead of regular neural network starting at 15:00. Does pieces of the image take the place of the pixels?
![The moment we stopped understanding AI [AlexNet]](/img/n.gif)
That's a masterpiece, not only have I learned how in detail convolutional neural networks work, but also I've learned how I should explain hard subjects to others. Thank you.
This is by far the best video I've seen on CNN. Thanks a lot!
I tend to get intimidated by videos longer than an hour, but I'm so incredibly glad I watched this one! Super clear explanation, I feel like I actually understand what happens now. No one else has been able to explain it so clearly. :) Thank you!!
That's so good to hear. I'm really happy that it clicked.
@@BrandonRohrer a little suggestion it would have been a lot better if it was a playlist consisting of 10 mins videos each, it would really be helpful for someone with low attention span like me
@@opto3539 Thanks Opto, I like this suggestion. I tried this on some later content and I like the result.
Although it's 5 years ago, this is the simplest and the AWESOMEST video in youtube for someone getting started with Computer Vision.
This lecture, along with 3-Blue 1-Brown neural network playlist, and you are good to explore
Thank you!!
That is a huge compliment. Thanks!
You're an amazing teacher. Just the right speed. The right structure. Well done.
This is the BEST video explanation EVER! Animation, simplicity, voice, oh god, you deserve an award in the machine learning world!
Thanks :) Made my day
Don't let the duration of this video intimidate you from enjoying this masterpiece of a presentation, just press play and begin, you'll freaking love every second of it.
Thank you so much for sharing this and so much other information for free!
One of the best videos I’ve ever seen on the topic: super clear explanation + truly in depth, all without being boring. The only thing I didn’t understand is how to determine the values in the matrices for the convolution.
Thank you Daniele!
The short answer: they start random and get adjusted during training by backpropagation ( e2eml.school/backpropagation )
The long answer: A two-course sequence walks through how to implement this in Python for 1-D ( e2eml.school/321 ) and 2-D ( e2eml.school/322 ) convolutional neural networks.
Thanks so much for spending time preparing this videos.
Watching is 1h, preparing for this video is probably * by 100 :)
3:20 - Filtering
8:10 - Pooling
10:30 - Normalisation (ReLu)
12:16 deep stacking
13:11 fully connected layers
17:00 receptive fields
18:00 create a neuron, create weight and squash the results (sigmoid function).
26:50 optimisation
and then he died
I had a diffi ultrasound time understanding the convolution layer, this course is the best among all courses I saw on TH-cam, keep the good work, you saved me , I was struggling understanding and now I'm completely clear. Thanks alot
There is lecturer that knows about what he's teaching the students. Well explained thank you.
I appreciate that.
I am a visual learner with no background of computer science and this video is a gem! Thank you very much. Subscribed:)
Thank you! I'm pleased to hear it.
I can't stress enough how great your videos and explanations are. I get overwhelmed by lots of text and missing visual examples, so it's great I found your videos. Watched 2 already and will definitely watch the rest too!
insanely good explanation, never seen anything like this. thanks a lot
For those who come from the shorter video by Brandon, the new stuff starts at 15:13.
Brandon, you explain the most difficult concepts in simple understandable language. Nice visualizations create a mind map which we cannot forget. Thank you for all your efforts on these videos!
Thank you so much Ishwar
This is by far the best explanation in convolution neural networks, gets into theory and details of things. The presentation of everything is superb. I now know precisely what CNN are exactly all about. I would never spend a full hour watching an explanation on youtube unless it is a full course. This explanation hour long of CNN is well worth it. Thanks.
Probably, one of the best intuitive explainers of why we like to use gradient descent in neural networks, which I ever seen.
This is the first video of yours I have watched. It was so good that I subscribed to your channel.
BYW, your voice is a lot like Brian Greene. This is good because it is a good lecture and documentary voice.
Thanks thomas, those are huge compliments. I'm really happy it was helpful.
this is the only explanation in youtube and the internet, that has finally helped to quench my thirst of understanding CNN!
Thank you!
Wow, i this tutorial is packed with information. I had to rewind a 100times to grasp the art about weights & errors, nobody ever explains this part for mere mortals like myself.
Thanks! I'm really happy to hear it.
Your explanation is amazing, from your video i can understand neural network. Thanks
Great presentation, Brandon. I prefer your simple graphics and pace over the highly distracting, animated videos from other educators.
Thanks! I appreciate that
You are simply the best at explaining this complex topic. Thank you.
Thanks!
Marvelous explanation, made simple and concise, yet not oversimplified to a level that would render it pointless. I could not have imagined a better way to bring the loose pieces in my head together. Thanks a lot for this.
one of the best videos about this topic I have ever watched. It is 1 in a thousand! Thank you for sharing it
Wow thanks!
First time replying to any tutorial in 7 years, You really know how to make others understand, Would love o work with you if I get a chance ever.
I'm so glad to finally find the videos about NN explained by somebody whose English I can understand.
I'm only halfway through but really, you're amazing at teaching and explaining concepts. Thank you
This is the best video I have ever watched about machine learning. You have more than just a talent.
I can't imagine how hard it was to make this cool video! Many thanks to the author!
I'm glad you enjoyed it!
It was nice of you to simplify the understanding as most TH-cam video's just put neural networks in an entertaining way with a vague explanation.
Just 10 mins into the video, I got a clear overall picture of CNN that I have searched for weeks. Thanks Brandon.
Detailed and concise at the same time. Perfect video.
A one hour well spent.,,in my Life...
Wow! All your perfect presentations combined in a better presentation! I'm bookmarking this one and also sharing it with my colleagues.
Explanation is on point!!!
damn, the most unexpected comment I have ever seen
Patec??
Very intuitive way of explaining Convolution Neural Networks. Great job!
This really make me understand CNN more intuitively, lucky to meet with your vedio😄
thank you so much Mr.Brandon Rohrer sir for your good teaching on convolutional neural networks.
thank you so much for your explanation. really helps me to understand what CNN is about
Very clearly spoken and illustrated. It's great to have well articulate and easy to follow tutorials like this.
THANK YOU THANK YOU THANK YOU. Finally I understood what Convolutional NN is. Great vid bro.
I'm so happy to hear it!
Thank you so much! I didn't have to pause once to understand anything. You explained it so perfectly.
This is the video I needed the most. Thank you
WoW! This is by far the best tutorial out there for CNNs! Thank you...
Jesus this was a fantastic tutorial I imagine you spent many months working on!
Awesome! I just didn't expect you to actually talk about backpropagation and linear layers but I'm not complaining.
Super Sir. Finally I got what I have expected.
Best explanation of how Neural Networks work I have watched so far! Well explained and really intuitive
Best explanation of backpropagation I've seen fr. Thank you SO much!
Thank you Berhane! I appreciate it.
I'm going to create a new account just to give this man two thumbs up. This lecture is soooo good.
Super! Crisp clear explanation with breaking down complex concepts into easily understandable steps.
Really good explanations. Just the right level of detail for my understanding. Thanks.
Very good tutorial. Learn so many things
Amazing dear...help alot to understand the foundation....👌
I'm happy to hear it
An incredible Video !! thanks brandon for such a good explanation to understand CNN. please don´t stop making more material . Greetings from Germany
Thank you Victor! I appreciate it.
Brandon beside knowledge also has nice narrative ability, for me definitely best 1 hour of time spent...
Underrated video! views should be at least E6.
Thanks Lee :)
@brandonrohrer sir in 14:27, are we evaluating the final confidence scores by taking the average of either x or o scores?
In this simplified example yes, but just a heads up that in practice it's often done just like the other layers - summing up all the inputs and passing them through an activation function, such as the logistic function.
Perfect !!!
Such a great video .
Thanks a lot Brandon
Thanks Omkar!
Great teachings !1h of Brandon = 15h of Stanford lectures....
Thank you so much SarahK
@@BrandonRohrer I would thank you much more for your efforts, you examples makes the subject so much easier digestible !
Fantastic video. The conclusion really summed up everything nicely.
Great work. Thank you so much. This has been the most useful video i have seen in NN!
I'm so happy to hear it.
What a great tutorial. Easily the best on CNN.
Wow, the explanation is easy to be understand. Thanks for your work. it helps me a lot
Sir, just finished watching and you explained this very well especially the second half with gradient descent and backpropagation. Thank you so much, have liked and Subscribed!
Thank you for this amazing video! It definitely helped clear a lot of stuff about CNNs for me. On a very random note, you have a great voice! I feel like you'd make an awesome audiobook narrator!
Aw thanks! That's a really nice ting to say.
quite good explanation Brandon !
Now i feel like sending CV to Tesla
Beautifully explained Brandon and so clear - thank you !
i loved your detailed explanation of the steps, but can you please make another video to explain the REASON for each of the steps in detail?
Thanks! If you want to go one level deeper, I recommend walking through e2eml.school/321 and e2eml.school/322 . They walk through the Python implementation and give a deeper understanding of how and why.
Thanks a lot !! You are one of the best teachers ever!!
Man, thank you so much!
This is incredible work!
Master piece!
One question: Is convolution the same or a kind of filtering?
Thanks! Here's a bit more on convolution that might help clarify: th-cam.com/video/B-M5q51U8SM/w-d-xo.html
And if you want to go really deep , there are courses here: end-to-end-machine-learning.teachable.com/p/321-convolutional-neural-networks
and here: end-to-end-machine-learning.teachable.com/p/322-convolutional-neural-networks-in-two-dimensions/
OMG, you are an amazing teacher. Thank you a million times
Thank you tran!
in 22:09 ,, the last layer, bottom right node, i think the 4 pixels need to be inverted to black is top, white is bottom... i am right?
amazing explanation with great examples
Wow! Very well done :) Perfect pace, content, and explanations.
That was AWESOME. The minor issue was, there was no pointer. ( We could skip the issue with the great explanation)
Many thanks :) And I agree. After this video I changed my workflow so that I could record a pointer too.
Wow !!!! Great tutorial, my knowledge expanded 10 fold
Magnificently explained sir, well done.
Thank you. I was in need for such a video. Well done.
The interaction with the audience feels so personal.
This is what a tutorial video should be!
I've learnt so much from these videos thanks a lot!!
I dont usually comment on youtube videos. All i can say is that you Sir!!!
one question @57:43 Convolutional Neuronal Networks can also be applied to 1D data for example audio right? It isnt necessary to convert the data to look like an image?! or i dont get it right. I mean if i have an array [1,2,3,4,5,6,7,8] i can apply 1D Convolutions on it .
You are right! 1D convolutions are not as common, but totally a thing. I'm just completing a course on building one from scratch in python to categorize electrocardiograms: e2eml.school/321 .
@@BrandonRohrer nice, i will check this course! keep doing the great work!
Great teacher! Big thank for your sharing to every body!
The best explanation I ever heard !!!!
This is such a clear explanation, thank you!!
its clear till 17:57 , but i just lost it at 18:01, just didnt understand why each lines there changed from 1.0 to -0.2 , 0.0 , 0.8 , -0.5.......can someone explain ?
Excellent . Thank you soooooooooooooooooooooooooooooooooooooooo much !😊😊😊
You are so very welcome :)
22:15 is wrong, the bottom of the 4 outputs is 'upside down'.. great video, though
Hi. Thanks for the great video. Just that in 22:20, I believe the last receptive field is wrongly visualized.
It would have been nice to see the in-depth breakdown of convolution layers instead of regular neural network starting at 15:00. Does pieces of the image take the place of the pixels?
Thank you Sir for this crystal clear explanation
Outstanding, many thanks for this educative video
This is great! Thank you Brandon.
You are very welcome Julie!
Sigmoid gives output between 0 and 1, I think the graph shown at 19:42 is of tanh
Thank you, it gave great clarity.