Really nice presentation for sure. Just one recommendation: please normalize your audio. It's super easy to do and makes people feel much more comfortable as they don't have to crank up the volume where any tiny notification will blow your eardrums to bits. I also feel like you could easily talk way faster, although adjusting playback speed manually does the trick just fine.
Thanks Min, and I appreciate the feedback. I've actually been speeding up my delivery just a bit in subsequent presentations. I was trying to keep the pace moderate for non-native English speakers, but went overboard. I like the audio normalization tip. I'll make sure to do that next time.
please re-upload this one with the audio made louder. btw, normalizing might not make it loud enough, feel free to make it louder by hand - the peaks will just be cut off, the speech will still sound good most likely. for best results use an audio dynamics compressor.
pace is no big deal, and it might even be better to err on the slow side. people who want it faster can watch at 1.25X or 1.5X speed, but 0.5x speed gets a little bit wonky. gotta love youtube.
Finally, someone who explained it in details, not some bunch of "CNN takes image, makes some stuff and poof it can classify" crap. Thank you, you are the hero wee need, not the hero we deserve.
Thanks Adam! Yours is officially the best compliment ever, narrowly beating out "The Mr. Rogers of Data Science." img0.etsystatic.com/065/1/9143987/il_570xN.761988760_8vsd.jpg
While my comment got your attention I will slip in an extra question for you, it's very clear for me how CNN can classify, but what about image processing? Can it do that (on it's own)? For example like noise removal or image colorization. It's like we would need to skip pooling, but then wouldn't we lose global information that we would need?
Don't be scared off by the length of this video. Excellent use of simple examples to illustrate core concepts. The author has a finely tuned sense of what to focus on and what to gloss over to impart the gist. Many explanations either get lost in (easily forgotten) jargon or muddied by too many details too fast. The overview of the limitations and application rules of thumb is especially valuable. Great stuff.
It honestly isn't lengthy for the content that is inside! People can search for years and struggle to find something that they can understand on this topics, and when they do, it takes a very long time to learn. This is only half an hour, shorter than a class that you would take at a university, but it is so worth it!
I'm a second year engineer from Germany and currently reading "Python Machine Learning" from Sebstian Raschka (newbie to programming as well!). Your videos clearly put all the pieces together. Thank you for being a great teacher :)
Brandon, Man, I have watched numerous videos but nobody explains it as cleary as you do. I beleive any in the IT world, watching your video will understand. Good job making it dumbproof and so within reach.
The best and the most detailed explanation I have ever heard. good to know you didn't rush, through the convolution, pooling or anything else! The example was great and easy to understand. Thumbs up!!!!
So for a while I had a basic understanding of this algorithm, but I would never have felt comfortable trying to make one on my own. Watching this video felt like all of the pieces were lining up together into a beautiful picture of how this algorithm works. This was really well done.
@@BrandonRohrer I'm trying to implement my own algorithm as well and I coudn't understand how would I make the algorithm decide which features are relevant. In your explanation, it seams to me that you choose them and not the algorithm itself
I don't know why but I teared up after understanding something so clearly. Like just how beautiful the intuition is, and I feel blessed to have something so amazing as my career, while half of the world is stuck in dull jobs they hate. You are a hero.
Sir, many people has already said that your video is the best, like many other videos about CNN get the same comments. But with me, i think your video is the best. Your voice, your slide, the video length,... is perfect. Thanks you so much sir.
One of the best videos out on the subject. The author is able to incrementally stack the concepts in an easy way to follow, introducing the complexity of the concepts in a timely and organized way that allow the viewer to follow and grow in understanding. Most of them out there jump to quickly to the math behind it where at the end you miss the point and don't know why all those complex steps. Thanks Brandon Rohrer for the this approach to explain this.
This is the best that I heard on convolutional NN. It is easy to describe difficult things in difficult ways. Much harder to do the same in simple words. You have nailed it.
Great content! Amazing visualization for all the maths! I finally understood what Convolutional Neural Networks are all about. Even after a whole course on ML at uni, I still couldn't get it, but a mere 26 minutes of your video did the wonder. Keep it up! :)
I'm non english speaker and I watched a bunch of videos on Neural Network but your video is by far the best one. I think I've manage to learn the weight thing. Thanks a lot.
A saying "Today's scientists have substituted mathematics for experiments, and they wander off through equation after equation, and eventually build a structure which has no relation to reality" by Tesla.... it is really true in the case of CNN concepts. Brandon.. you have structured the concepts of CNN so simple and easy to understand in this tutorial. Best video on CNN so far. Looking fwd for more video sessions from you !!!
actually it has a "relation to reality" as it implements our object classification in our brain. the convolutions' output "feature maps" are bigger and bigger features - like lines, curves, then bigger and bigger parts of the object - we recognize and that's how we classify the objects too. pooling helps us recognize them invariant of angular etc. differences, like if I hold up a shoe for you sideways, you'll still know it's a shoe
This is seriously the best video on CNNs!!! I cannot believe the skills you have to make this soo easy to understand!! Thank you so much, everything is so clear to me now : )
These 26 minute video clarified a concept that days of research elsewhere hasn't been able to so concisely and intuitively, so it helps me understand rather than just be able to "replicate" a model. Thanks Brandon, this was brilliant!
@@adamboris7925 How so? I wish people would say I am cute. Don't you? Plus I don't think Tres is looking for a partner over youtube lol. I think it was meant more for comedic relief after watching a 30 minute video on machine learning.
Talk about straight forward. Yes! I have been struggling with all the terminology and basic language in this science to find out that its not that hard to understand at all. THANK YOU.
Fantastic explanation. There were some things that weren't clear enough for me (how the convolutional filters were learned via backpropagation) but you explained everything so clearly that this is even usable by people with no prior ML or ANN background. Thanks for posting this, Brandon!
this is the best presentation of cnns i've seen so far. good job. since it was so good, i wish you explained backpropagation a little bit more because this is something i still dont know how it works in detail. there can be so many layers and weights, how you know which one you have to adjust?
Thanks Windar, I'm glad it was helpful! And thanks for the +1 on backpropagation. I've added "How Backprop Works" to my to-do list. The really short version is that every weight in every layer gets adjusted a little bit every time. The slightly longer version is that it helps if you randomly select half the weights in every layer to adjust and pick a new set each time. This is called "dropout" and it helps avoid overfitting.
wow, even in 1-2 phrases you can explain more than other guys in 1 hour presentations. "How Backprop Works" from you would be nice, but dont do it just because of me. i subscribed anyway!
You compute the derivative of each unit from back to front. The derivative is really a slope. The slope then tells you what direction (+ or -) you should adjust the weight in order to improve the output of this particular unit. Do this across the whole network and you've improved the output of the whole network. Check out karpathy's blog: karpathy.github.io/neuralnets/
I watched series of videos and I was trying to find one that covers the core concepts without going into details and I could not. Finally, I came up with this video. I am sure you knew what other videos are missing and you have covered all here ! Good job
Best video about CNNs i've seen so far. I am new to coding (been doing it for 6 months) but i still understood how it works, and how it might look when written down in my code editor. Awesome job! Thanks a lot!
You are a lifesaver!!! I have to elaborate a paper on CNNs for uni (ImageNet Classification with Deep Convolutional Neural Networks) and it really got me confused with all those new terms and general complex language (engl. is not my first language). Thise video helped me even more than I hoped! Thank you!!
This is absolutely a very good explanation with respect to Convolutional Neural Networks. The way the whole thing is broken down, discussing the foundational aspects and finally building them up to show how learning / predictions occurs. Thank you Brandon
Thank you very much! It's so clear and well-structured. 20 minutes worth of a month of some online courses. As a non-native speaker I appreciate the moderate speed.
I'm having to help prosecute patents involving neural networks. Your videos have been amazing in helping get an (at least top-level) understanding in my head quickly. Thanks a ton!
There are many explanations about ConvNets around, but they always seem to miss the point that the filters are also learned. Congrats for your detailed and kind tutorials. Thank you.
at 17:20, you say that the features themselves can be learned by the backpropagation step. how does it do that? how does it know that diagonal 3x3 features and 3x3 x's are the best features for this application? why isn't it one of the hyperparameters? thank you for the great video btw!!!
Thanks William! There are a couple of other videos that answer your question at the next greater levels of detail and depth. How backpropagation works (ruclips.net/video/6BMwisTZFr4/видео.html) and How 1D convolution for neural networks works (ruclips.net/video/4ERudRAxyGE/видео.html). In this example, it wouldn't actually learn diagonal and x-shaped features. Those are hand crafted to help illustrate the concepts.
This is the first time I've really felt like I "got" what was going on and why so many talks emphasize image analysis. Thank you for tying it back to language and sound as well.
Just wanted to drop a quick note to say thanks for your sick video on CNNs - it was the clearest explanation I've seen! Your examples were so relatable and the visuals really helped it all click for me. Keep crushing it!
By far the best explanation I got watching your video about CNN ... your clear, concise and easy to understand style of teaching makes even a convoluted concept like CNN so easy to grasp the very first time. Thank you very much!
This simply is just amazing... i spent the whole week to understand this subject with many videos and reading materials....non can come as easy and understandable as this one.. Good job ...and thanks a lot....
thank you! this is by far the best intro. Now, what would be even cooler is to show the actual code like in Keras. This would make things even more clearer.
Dear Brandon, thank you for your video, I just started studying machine learning and your videos help a lot! In my humble opinion I've noticed a small typo in this video. On the 18:05 minutes we can see a table, the last column contains Errors. I think the right value in the second row(error for 'O') is 0.51(abs(0 - 0.51)). Thank you one more time for your awesome videos!
![Blox Fruits Dragon Rework Update [Full Stream]](http://i.ytimg.com/vi/EqDAp8udhm0/mqdefault.jpg)
i've watched a good dozen of videos about convolutional neural networks but you've nailed it!!!
thx for the insights
Thanks Ivan :) I'm very happy to hear it.
Really nice presentation for sure. Just one recommendation: please normalize your audio. It's super easy to do and makes people feel much more comfortable as they don't have to crank up the volume where any tiny notification will blow your eardrums to bits.
I also feel like you could easily talk way faster, although adjusting playback speed manually does the trick just fine.
Thanks Min, and I appreciate the feedback. I've actually been speeding up my delivery just a bit in subsequent presentations. I was trying to keep the pace moderate for non-native English speakers, but went overboard. I like the audio normalization tip. I'll make sure to do that next time.
please re-upload this one with the audio made louder. btw, normalizing might not make it loud enough, feel free to make it louder by hand - the peaks will just be cut off, the speech will still sound good most likely. for best results use an audio dynamics compressor.
pace is no big deal, and it might even be better to err on the slow side. people who want it faster can watch at 1.25X or 1.5X speed, but 0.5x speed gets a little bit wonky. gotta love youtube.
I have a report on neural networks due 3 days into the semester so this helps and makes me not want to die as much as before.
what are you studying?
Damn, dude. Didn't expect to see you here!
What was your report on? Melon classification using NN?
Cyranek meme god
Made my end project in 3 days
Finally, someone who explained it in details, not some bunch of "CNN takes image, makes some stuff and poof it can classify" crap. Thank you, you are the hero wee need, not the hero we deserve.
Thanks Adam! Yours is officially the best compliment ever, narrowly beating out "The Mr. Rogers of Data Science." img0.etsystatic.com/065/1/9143987/il_570xN.761988760_8vsd.jpg
While my comment got your attention I will slip in an extra question for you, it's very clear for me how CNN can classify, but what about image processing? Can it do that (on it's own)? For example like noise removal or image colorization. It's like we would need to skip pooling, but then wouldn't we lose global information that we would need?
Yes. CNN explained in details, step by step. I appreciate it very much!
the most comprehensive guided tour ever done about convolutional Neural Networks, and within less than 30min. Simply brilliant !
Thanks olivier! It's also a huge honor to get a nod of approval from Ted.
Don't be scared off by the length of this video. Excellent use of simple examples to illustrate core concepts. The author has a finely tuned sense of what to focus on and what to gloss over to impart the gist. Many explanations either get lost in (easily forgotten) jargon or muddied by too many details too fast. The overview of the limitations and application rules of thumb is especially valuable.
Great stuff.
It honestly isn't lengthy for the content that is inside! People can search for years and struggle to find something that they can understand on this topics, and when they do, it takes a very long time to learn. This is only half an hour, shorter than a class that you would take at a university, but it is so worth it!
I had bookmarked this post, came back after 2 years and found I understand it so much better. Wish I could give it a second thumbs up. Thanks!
I'm glad to hear it :) Welcome back.
I'm a second year engineer from Germany and currently reading "Python Machine Learning" from Sebstian Raschka (newbie to programming as well!). Your videos clearly put all the pieces together. Thank you for being a great teacher :)
Brandon,
Man, I have watched numerous videos but nobody explains it as cleary as you do. I beleive any in the IT world, watching your video will understand. Good job making it dumbproof and so within reach.
The best and the most detailed explanation I have ever heard. good to know you didn't rush, through the convolution, pooling or anything else! The example was great and easy to understand. Thumbs up!!!!
I'm happy it was clear Sahil. That is my number one goal.
The example with X and O was a brilliant approach. This is where most machine learning videos miss out.
So for a while I had a basic understanding of this algorithm, but I would never have felt comfortable trying to make one on my own. Watching this video felt like all of the pieces were lining up together into a beautiful picture of how this algorithm works. This was really well done.
Thanks +askmiller. You made my day.
@@BrandonRohrer I'm trying to implement my own algorithm as well and I coudn't understand how would I make the algorithm decide which features are relevant. In your explanation, it seams to me that you choose them and not the algorithm itself
Every slide that is presented here opens up a new dimension of understanding about the CNNs. Awesome job!!!
Best visualization of convolution I've seen.
by far the best video ever! please please do part 2 with code now. love it !!!!!!!!
Many thanks! The with-code version is more involved: e2eml.school/321
I don't know why but I teared up after understanding something so clearly. Like just how beautiful the intuition is, and I feel blessed to have something so amazing as my career, while half of the world is stuck in dull jobs they hate. You are a hero.
Sanket Shah, your comment made me tear up too. I'm very happy the video helped.
Sir, many people has already said that your video is the best, like many other videos about CNN get the same comments. But with me, i think your video is the best. Your voice, your slide, the video length,... is perfect. Thanks you so much sir.
By far, the best explanation over the web.
One of the best videos out on the subject. The author is able to incrementally
stack the concepts in an easy way to follow, introducing the complexity of the
concepts in a timely and organized way that allow the viewer to follow and
grow in understanding. Most of them out there jump to quickly to the math behind
it where at the end you miss the point and don't know why all those complex steps.
Thanks Brandon Rohrer for the this approach to explain this.
Thanks Behezinam! I am very happy to hear it :)
You make complicated things look quite easy to understand
I appreciate it Youssef
Thats really true
This is the best that I heard on convolutional NN. It is easy to describe difficult things in difficult ways. Much harder to do the same in simple words. You have nailed it.
very good video, finally someone explained it clearly. Thank you !
Thank you Wang :) I'm happy it was clear.
i've spent hours and hours trying to figure out what CNNs are , but these 30 minutes is the one that fixed my issue , thanks a lot Sir !
Great content! Amazing visualization for all the maths! I finally understood what Convolutional Neural Networks are all about. Even after a whole course on ML at uni, I still couldn't get it, but a mere 26 minutes of your video did the wonder. Keep it up! :)
This was better than any Stanford or Harvard lecture on this topic. Well done! Thank you!
Wow, thanks Gabe! I'm glowing.
Best explanation for Convolutional Neural Networks ever seen
I'm non english speaker and I watched a bunch of videos on Neural Network but your video is by far the best one. I think I've manage to learn the weight thing. Thanks a lot.
Please make more videos like this. You helped me understand concept which I was learning for months in just 26 min.... Thanks Sir..
This is the best and most clear explanation of back propagation and how neural networks works. Thank you for such excellent work.
A saying "Today's scientists have substituted mathematics for experiments, and they wander off through equation after equation, and eventually build a structure which has no relation to reality" by Tesla.... it is really true in the case of CNN concepts. Brandon.. you have structured the concepts of CNN so simple and easy to understand in this tutorial. Best video on CNN so far. Looking fwd for more video sessions from you !!!
Thanks for the wisdom Kiran. Tesla is a hero of mine.
actually it has a "relation to reality" as it implements our object classification in our brain.
the convolutions' output "feature maps" are bigger and bigger features - like lines, curves, then bigger and bigger parts of the object - we recognize and that's how we classify the objects too.
pooling helps us recognize them invariant of angular etc. differences, like if I hold up a shoe for you sideways, you'll still know it's a shoe
This is seriously the best video on CNNs!!! I cannot believe the skills you have to make this soo easy to understand!! Thank you so much, everything is so clear to me now : )
Best video till now ... which I saw for CNN ... Great..
You sir, are the best minimalist teacher I have found on the internet. For a beginner you are a blessing!!
This video saved my semester.
What are you studying?
Very useful and clear presentation
same
These 26 minute video clarified a concept that days of research elsewhere hasn't been able to so concisely and intuitively, so it helps me understand rather than just be able to "replicate" a model. Thanks Brandon, this was brilliant!
ty .. this answers some of my questions about cnns regarding pattern recognition (the filtering steps)..
You took a subject many others struggle to explain (also here in youtube) and what you made is art.. the are of simplification.
Many thanks buddy!
beatifull video man, i see you spend a lot of time making it. I enjoyed it very much, thank you :D
THE BEST introduction to convolutional neural networks for BEGINNERS!!! Many many thanks!
1) You're cute
2) The graphics are great
3) The video was very informative
4) The sound had a bit of echo
Tres Kewlshoes first part is disrespectful and inappropriate.
@@adamboris7925 How so?
I wish people would say I am cute. Don't you?
Plus I don't think Tres is looking for a partner over youtube lol. I think it was meant more for comedic relief after watching a 30 minute video on machine learning.
Randy Diaz cute sounds gay
@@ChessCat1500 you're gay to find it gay
Talk about straight forward. Yes! I have been struggling with all the terminology and basic language in this science to find out that its not that hard to understand at all. THANK YOU.
Great introduction to ConvNets! Thanks a lot.
Please also make similar videos about RNN, LSTM, etc.
Also please if possible do a series on Gradient descent algo's such as momentum, AdaGrad, Adam etc. with mathematics
+1
Clear description of what's happening behind the scene. Thanks for the great tutorial!
Seriously my graduate level class on Image Processing has spent two lectures explaining this and only now it finally clicks!
I'm glad to hear it Joel! Hang in there.
I would say the clearest introduction to the subject I have ever watched, thank you.
Best Video I ever seen about neural network....thanks for uploading Brandon
Fantastic explanation. There were some things that weren't clear enough for me (how the convolutional filters were learned via backpropagation) but you explained everything so clearly that this is even usable by people with no prior ML or ANN background. Thanks for posting this, Brandon!
Thanks Ahmed! That's exactly what I was aiming for. I'm glad it helped.
When you used the filter/kernel, why did you average the results?? Everywhere else I've looked there's only the sum
Hands down one of the greatest educators of the 21st century.
this is the best presentation of cnns i've seen so far. good job.
since it was so good, i wish you explained backpropagation a little bit more because this is something i still dont know how it works in detail. there can be so many layers and weights, how you know which one you have to adjust?
I was thinking the same. How backpropagation, or any other learning algo, works with this?
Thanks Windar, I'm glad it was helpful! And thanks for the +1 on backpropagation. I've added "How Backprop Works" to my to-do list. The really short version is that every weight in every layer gets adjusted a little bit every time. The slightly longer version is that it helps if you randomly select half the weights in every layer to adjust and pick a new set each time. This is called "dropout" and it helps avoid overfitting.
wow, even in 1-2 phrases you can explain more than other guys in 1 hour presentations.
"How Backprop Works" from you would be nice, but dont do it just because of me. i subscribed anyway!
You compute the derivative of each unit from back to front. The derivative is really a slope. The slope then tells you what direction (+ or -) you should adjust the weight in order to improve the output of this particular unit. Do this across the whole network and you've improved the output of the whole network. Check out karpathy's blog: karpathy.github.io/neuralnets/
26+ min video but so dense with information, well done sir!
I appreciate, professor^^ from S.korea
chang yong kang 여기서 뵙네요...하핳😄
@@korean4130 님 저도 치과 진단에 도움이 될까 해서 관심을 ㅎㅎ^^
m.blog.naver.com/nkabcd2/221637820902
Can you create brain-computer interface?
Hello from 청주시 haha
@@austinbecton5341 ^^ welcome ^^ have a good day ^^
I watched series of videos and I was trying to find one that covers the core concepts without going into details and I could not. Finally, I came up with this video. I am sure you knew what other videos are missing and you have covered all here ! Good job
beautiful explanation! 10/10
Well thank you very much!
Best video about CNNs i've seen so far. I am new to coding (been doing it for 6 months) but i still understood how it works, and how it might look when written down in my code editor. Awesome job! Thanks a lot!
Thank you Jonas! I'm really happy it has been helpful.
Brandon, this is really good. Thank you very much!
definitely one of the most simple and useful videos, if you're trying to understand something about it
You nailed it, best video in CNN so far, at lest for a ML newbie like me.
This is the simplest video i 've seen on CNN, by simplest I mean explaining all the concepts in a simpler manner. Thaks a ton .
It's inaudible :(
I mean knowing how to simplified the obvious things is an ART. Thanks for the video.
Many thanks, Ali.
The volume is too low, I can't even hear you!
your way of explaining things using live examles is amazing, thanks for the video
Thank you Srivastava :)
When I'm looking at pornography, my brain votes 1.00 that this is pornography. ;)
The flow of your explanation was neat and point to point thanks a ton!
Haven't even finished watching this, just to comment. It's BRILLIANT! Very well explained. Thank you
Best video I've found on CNNs. Thanks for taking the time to make this topic more accessible!
This Guy Deserves a Noble Prize for Teaching!
I am new to neural networks and found this one of the clearest tutorials ever. Very good.
Your video is the best illustration of CNN I found so far
You are a lifesaver!!!
I have to elaborate a paper on CNNs for uni (ImageNet Classification with Deep Convolutional Neural Networks) and it really got me confused with all those new terms and general complex language (engl. is not my first language).
Thise video helped me even more than I hoped! Thank you!!
This is absolutely a very good explanation with respect to Convolutional Neural Networks. The way the whole thing is broken down, discussing the foundational aspects and finally building them up to show how learning / predictions occurs. Thank you Brandon
Thanks Ravisankar! I'm really happy you found it helpful.
its so clear after this video, pictures are great and the professor even better.
Thank you so very much
extremely helpful...most clear and crisp explanation i found so far on the working of the convolutional neural nets...thanks
Thanks! I'm really happy it worked for you Abhijit.
I am glad that I understood CNN finally with the help of this video, You are exceptionally great tutor. Thank you so much.
The best intro for CNN indeed. Plz keep going and publish more intro vids about other networks like Boltzman machines, Recurrent nets etc. Tanx a lot!
This was --hands down-- the best intro video on the topic I have seen yet. Great work. Was a fan of your videos at Microsoft
clear crystal presentation - amongst the best video on CNN I have seen. Thanks!
Thank you very much! It's so clear and well-structured. 20 minutes worth of a month of some online courses. As a non-native speaker I appreciate the moderate speed.
Thanks Ivan. That's really helpful feedback. I'm glad you appreciated the content and the speed.
This is by far the best video about neural networks I've ever seen. Simply awesome! :)
I'm having to help prosecute patents involving neural networks. Your videos have been amazing in helping get an (at least top-level) understanding in my head quickly. Thanks a ton!
I'm very happy to hear that it's been helpful :)
Clear, concise, and very well organized. Props.
There are many explanations about ConvNets around, but they always seem to miss the point that the filters are also learned. Congrats for your detailed and kind tutorials. Thank you.
great speed, well chosen words and figures. This is a perfect explanation video
at 17:20, you say that the features themselves can be learned by the backpropagation step. how does it do that? how does it know that diagonal 3x3 features and 3x3 x's are the best features for this application? why isn't it one of the hyperparameters? thank you for the great video btw!!!
Thanks William! There are a couple of other videos that answer your question at the next greater levels of detail and depth. How backpropagation works (ruclips.net/video/6BMwisTZFr4/видео.html) and How 1D convolution for neural networks works (ruclips.net/video/4ERudRAxyGE/видео.html). In this example, it wouldn't actually learn diagonal and x-shaped features. Those are hand crafted to help illustrate the concepts.
@@BrandonRohrer thank you! I'll check those out!
This is the first time I've really felt like I "got" what was going on and why so many talks emphasize image analysis. Thank you for tying it back to language and sound as well.
That's wonderful Bellabellie! I'm pleased to hear it.
You explained this better than any of my professors. Instant subscribe!
With just first 9 minutes of video, I was so sure I will understand the rest of the video. Thank you!
This is the best tutorial in this topic i've seen so far. Thanks for sharing!
I agree with most people. The explanation about Convolutional Neural Networks made me finally understand how they work! Thanks!
EXCELLENT explanation without maths of the how. Great job.
Just wanted to drop a quick note to say thanks for your sick video on CNNs - it was the clearest explanation I've seen! Your examples were so relatable and the visuals really helped it all click for me. Keep crushing it!
Thanks Jessica. I'm really happy to hear it!
By far the best explanation I got watching your video about CNN ... your clear, concise and easy to understand style of teaching makes even a convoluted concept like CNN so easy to grasp the very first time. Thank you very much!
This might be the best video on CNNs I have ever watched. Keep up the good work!
This video explains really well, those terms and intuition. better than a lot of online courses.
Thank you for this awesome video. I've spent more than 2 months trying to get ConvNets with no luck, but now I do!!!
That's awesome Yaison. I'm happy to help!
This simply is just amazing... i spent the whole week to understand this subject with many videos and reading materials....non can come as easy and understandable as this one.. Good job ...and thanks a lot....
Thank you Abel. I'm happy it helped bring the concepts together for you!
Thanks again...I will be happy if there is an extension of this on the topic of "transfer learning" and its implementations with some example...
thank you! this is by far the best intro. Now, what would be even cooler is to show the actual code like in Keras. This would make things even more clearer.
Thanks Konstantin! I greatly appreciate it. I'll put "show coding examples in Keras" on my To Do list.
Dear Brandon,
thank you for your video, I just started studying machine learning and your videos help a lot!
In my humble opinion I've noticed a small typo in this video. On the 18:05 minutes we can see a table, the last column contains Errors. I think the right value in the second row(error for 'O') is 0.51(abs(0 - 0.51)).
Thank you one more time for your awesome videos!
Thanks for the catch Anton! I appreciate your careful viewing of the video. I'm very happy it has been helpful.
One of the best explanations I have found..hats off