I have no words to thank you enough. I have read several articles on major platforms but none of them explain things as clearly and in as simple terms as you did. May God make your mission of educating full of prosperity and success.
I'm a korean. I've searched many lectures for understanding CNN. none of them is better than this lecture. Thank you so much for easy and clear lecture~!!
I have watched many videos on CNN , but nobody ever explained it like this . I have no words to describe how good your explanation is sir . Hats off to you.
What kind of teacher you are? the best and authentic one !! I agree with people commenting this 21 min video is better than 3 lakh data science course and also you save 10 months.. Apart, you are guys India is always proud off
This is incredible. Fully explained the math behind how CNN Kernels, strides, padding, pooling, and flattening work with simple matrix examples. I've watched many videos about this process and nothing explained it as well as this. Thank you so much !!!!!
Thank you for nice words. Happy to know my hard work behind this video paid off with all great and nice words from many of you. Keep suggesting to improve this channel and keep learning !!
Glad to hear Tuhin, this Convolutional Neural Network CNN Tutorial series helped you. Keep Learning, Keep Learning and thank you very much for your nice words !!
easy to understand i was struggling to understand the basics and the maths behind CNN and you made it so easy that now i can explain it to my friends too and even teach them the basics.
I am delighted to hear that my CNN video has been helpful for your studies. Your comment and the time you took to share your feedback mean a great deal to me. Knowing that my video has made a positive impact is truly rewarding. Thank you for your support and for taking the time to reach out.
Good explanation. Except 3 things that I don't understand: 1) what numbers do you put in kernel ? I mean what you are gonna filter the input image with ? 2) at 19:50 you said you have 32 filters, why 32 ? and again what numbers you put in those filters ? there are quite many... 3) and last, you have a fully connected neural networks, lets say it outputs 2 answers (yes or no, having 2 output nodes), but how are you dealing so many YES and NOs that comes from every flattened arrays just before the neural networks ? Do you average them ? How do you get just one answer at the end ?
I am really Thankful to You sir. You are really a professional Teacher. Explain the Complex topic in Very effective and easy Way. Thank you Sir. 💚Love from Pakistan 💚
Thank you so much for your kind words and support! It means a lot to me to hear that my content is resonating with you and motivating you. Your comment has truly made my day and inspires me to continue creating and sharing. Thanks again for taking the time to share your thoughts, and I hope to keep delivering content that you find valuable and enjoyable.
Your feedback is very valuable to me, and it encourages me to continue making videos. I hope you'll stick around for more content, and I look forward to hearing your thoughts in the future. Thanks again for your support!
Such a Wonderful teacher - you saved me from reading so many articles in the middle of the night when I am feeling so sleepy but your lecture was so good that I didn't want to lose focus. Thank you for making me understand the CNN concepts so clearly- can't thank you enough 🙏🏻
Hi, the procedure to find the convolutional directly by multiplying the image pixel values with the kernel coefficient values and then add them , is valid if the kernel is symmetrical in nature. If it not so, then rotate the kernel horizontally and vertically by 180 degree rotation then we can use kernel to find the convolutional. So, in the video 6:00 to 7:00, the kernel must be symmetrical if you find the convolutional directly. Please correct it.
Your feedback is very valuable to me, and it encourages me to continue making videos. I hope you'll stick around for more content, and I look forward to hearing your thoughts in the future. Thanks again for your support!
I have no words to thank you enough. I have read several articles on major platforms but none of them explain things as clearly and in as simple terms as you did. May God make your mission of educating full of prosperity and success.
+1
+2
Thats true
+1000000000
I'm a korean. I've searched many lectures for understanding CNN. none of them is better than this lecture. Thank you so much for easy and clear lecture~!!
I have watched many videos on CNN , but nobody ever explained it like this . I have no words to describe how good your explanation is sir . Hats off to you.
The best video in all UA-cam about CNN
Yrah. Complete explanation
No cap
Ye kernel value kha se aya
What kind of teacher you are? the best and authentic one !! I agree with people commenting this 21 min video is better than 3 lakh data science course and also you save 10 months..
Apart, you are guys India is always proud off
Thank you Raj for wonderful words. Your comment add some more joy in my today birthday celebration 🎉👏🤝🙂
@@binodsuman Happy Birthday. Sorry for the late wish
This is incredible. Fully explained the math behind how CNN Kernels, strides, padding, pooling, and flattening work with simple matrix examples. I've watched many videos about this process and nothing explained it as well as this. Thank you so much !!!!!
you explained so well. Way more better than foreign university professors
This is one the greatest CNN video I have ever seen
Thank you for nice words. Happy to know my hard work behind this video paid off with all great and nice words from many of you. Keep suggesting to improve this channel and keep learning !!
Thank you so much man. I learned CNN basics in just 29 minutes. You are really awesome
I like your old school type of teaching with little bit of definitions on screen. Thanks sir.
The best video to understand cnn from scratch..... God bless you sir
Excellent! None of the graduate professor here at my university in the US taught this concept so easily.
you made the explanation so easy i have been scrolling through webs and videos but here you gave the answer in simple way may May God bless you.
This is the best explanation ever. Thank you so much for this
Thank you so much for this nice explanation. 21 minutes more beneficial than my whole deep learning course.
Thank you Nafas for nice words. Really motivating me to do more. Happy to know this Deep Learning concept helped you. Keep Learning !!
@@binodsuman do more and more 🥰👍
What an explanation sir , thank you very much. It didn't felt like 20 mins at all. Great explanation. Thankyou
I spend my whole day for one by one concepts in different videos. Alas l found you and can do this work in 21 minutes.
very easy to understand, thanks for the explanation. my research became easier with this video. may god bless you.
Best video on CNN ever...!!!!!
I am from Bhutan
You save our life in project
Happy to hear that my effort in creating this CNN UA-cam video was helpful to you. Wishing you all the best with your project.
Hello sir at 11:31. The value in the 1st row 2nd column is supposed to be 17 not 7 in the 2x2 matrix. Thanks for the tutorial. You did a great Job👏
You're the best....finally an explanation worth listning to
sir.. thanks a lot to end my 10 months-long struggle to end this. you are doing a great job sir.. no thanks would be enough.. respect from netherlands
Glad to hear Tuhin, this Convolutional Neural Network CNN Tutorial series helped you. Keep Learning, Keep Learning and thank you very much for your nice words !!
Couldn't be more simpler. Excellent. It has helped me a lot in understanding deep learning. Thank you!
easy to understand i was struggling to understand the basics and the maths behind CNN and you made it so easy that now i can explain it to my friends too and even teach them the basics.
This is one of the best explaination of CNN. Thank you very much
One of the most understandable videos about CNN. Thanks much ^^
Shandaar...Finally i got a channel with ease of understanding. Now I can get things more clearly. Thanks BINOD SUMAN ACADEMY.
I spent a lot of time understanding this but I'm still not satisfied but your video gives my answer thanks for a great video
Overwhelmed sir , nicely explained a difficult topic
16:19
Easiest explaination ever!! Thankyou so much sir. I understood it really well. It helped me alot.
I am delighted to hear that my CNN video has been helpful for your studies. Your comment and the time you took to share your feedback mean a great deal to me. Knowing that my video has made a positive impact is truly rewarding. Thank you for your support and for taking the time to reach out.
Extremely useful video on CNN for beginners! Thankyou!
This is the first time I understood anything about this topic. May all your dreams come true good sir
The best way to start from null to deep..Thank you sir...
Crystal clear explanation. Thanks.
watched this video literally 2hrs before exam and scored full marks. Kudos to you sir.
I am agree! 🥺
Broooo sameeeeeee💥💥
SIR please pin my comment 😩
@@sirirayapati9955 sir,plz do the needful😌🥺
very sincere effort to make the basics crystal clear
Good to know Somasekhara, this Convolutional Neural Network CNN Tutorial Series helped you. Keep Learning !!
Woww what a wonderful explanation it's soo beautiful you are really the best
BEST Video till now for CNN
believe me the way u explained this is excellent.....thanks bro
Simply amazing , I have test tomorrow and this video is a life saver.
I got good knowledge about CNN after watching this video..
Good to know, this CNN video was useful for you. Keep Learning !!
Thanks you so much for this tutorial, Love from BANGLADESH
The best video for CNN. Thank you.
Sir.......... Thank you so much sir. Its very easy to understand. Thank You again sir
Thank you for your Good teaching methods ❤
Grateful to you for this video.
Such a clear and detailed explanation! One of the best videos I have seen on Convolutional Neural Networks.
Good job Binod - A friend from BITS
Thank you so much for kind words.
bahut aacha explain hai master jii awesome
Thanks Binod, fantastic explanation. Keep up all good work.
Nice job. You will become more famous, you deserve it.
It is an great introduction and excellent refresher on CNN..
Nice to know this CNN Tutorial video helped you. Thank you Brahma for appreciation !!
Really enjoyed how you made it so clear. Thank you.
amazing content .
Good explanation. Except 3 things that I don't understand:
1) what numbers do you put in kernel ? I mean what you are gonna filter the input image with ?
2) at 19:50 you said you have 32 filters, why 32 ? and again what numbers you put in those filters ? there are quite many...
3) and last, you have a fully connected neural networks, lets say it outputs 2 answers (yes or no, having 2 output nodes), but how are you dealing so many YES and NOs that comes from every flattened arrays just before the neural networks ? Do you average them ? How do you get just one answer at the end ?
absolutely good explanation, helped me.
Very good lecture .Thank you so much.
Wonderful video. Easy to understand. Highly recommended.
I am really Thankful to You sir.
You are really a professional Teacher. Explain the Complex topic in Very effective and easy Way. Thank you Sir.
💚Love from Pakistan 💚
Very nice explanation Sir. Thank you
very clear explanation. Thank you.
Clearly explained CNN . Thanks a lot… I would like to know how kernel values are selected initially
before watching this video I was so confused about this different terminology, but sir you make the thing easy for us. thank you and stay blessed
Thank you so much for your kind words and support! It means a lot to me to hear that my content is resonating with you and motivating you. Your comment has truly made my day and inspires me to continue creating and sharing. Thanks again for taking the time to share your thoughts, and I hope to keep delivering content that you find valuable and enjoyable.
thank you so much sir
You're simply the best!
he earned my subscription
It's really very much helpful .A clear explanation with example you did.Thanks from me Bangladesh a lot.
Very nice explanation. Didn't hear clearly where the "32" at 19:51 comes from.
he is taking 32 different kernels or filters
you take as per ur requirements......the number of features you want to extract from the image
Thank you for very clear explanation on CNN
Good to know Dr. Chaluvadi, this Deep Learning CNN Tutorial series helped you. Thank you for your nice words !!
Thanks a ton Sir ! U made this topic so easier to grasp :)
Wonderful explanation
Hats off to you.
Your feedback is very valuable to me, and it encourages me to continue making videos. I hope you'll stick around for more content, and I look forward to hearing your thoughts in the future. Thanks again for your support!
Very nice explanation. Thank you
Thank you for making this accessible and easy to understand! Very helpful.
Such a Wonderful teacher - you saved me from reading so many articles in the middle of the night when I am feeling so sleepy but your lecture was so good that I didn't want to lose focus. Thank you for making me understand the CNN concepts so clearly- can't thank you enough 🙏🏻
Exactly my dear. I have watched several videos... he just saved me.
Hi, the procedure to find the convolutional directly by multiplying the image pixel values with the kernel coefficient values and then add them , is valid if the kernel is symmetrical in nature. If it not so, then rotate the kernel horizontally and vertically by 180 degree rotation then we can use kernel to find the convolutional. So, in the video 6:00 to 7:00, the kernel must be symmetrical if you find the convolutional directly. Please correct it.
That's was the best explanation i've seen
Sir u cleared everything and your slides will help in writing exam
Thank you for making this sooo simple!! dhanyabaad sir !!! :)
Best video on internet
Your feedback is very valuable to me, and it encourages me to continue making videos. I hope you'll stick around for more content, and I look forward to hearing your thoughts in the future. Thanks again for your support!
Very clear explanation. Thank you sir.
Thanks for providing such precious content.... Good Wishes
It's my pleasure and glad to this CNN tutorial series you liked it. Keep Learning !!
beautiful illustration and easy explanation....good work
Many thanks for that awesome explanation
Thank you so much for this magnificent tutorial, your efforts are much appreciated!
So much amazing and informative video and i really learned a lot of things from this video
Thank you Omair for wonderful motivation 👍. Happy to know this CNN tutorial video was helpful, keep learning.
Very well explained sir.it could not be easier than this
Good to know, Abhishek. Happy to see these important Deep Learning concept video helped you. Good to read your comment. Keep Learning !!
thank you so much sir for this amazing knowledge
Bahut acha samjhaaye aap bhaachaa....
Thala... Super explanation thala
Excellent way of teaching
Thank you very much guruji it was great help 🙏🏻
You are a great teacher
The best explanation ever
Excellent explanation sir🙏🏻. Thank u sir....
best video!!!! very simply explained ...Thanks a lot sir
You are doing a great job sir. Please keep it up.
nucely explained all together... thank you sir...
The best all doughts cleared 😊❤❤❤
Thank you so much sir
I had a seminar on this topic so this video is very useful for me to understand and explain