If you only watch videos on topics that you relate to, then how do you learn about other topics? I think it is very important for us to learn about various topics so we can find the one that really interests us.
Thank you so much for this detailed and easy to follow demonstration! It's a major component of my grad research and you have tied the concepts together so well that it really complements and reinforces my understanding.
Very helpful video. Can you please tell me that can we perform semantic segmentation using conditional GAN. In this video, you talk about getting real image from semantic segmented image. But can we perform the task we did using UNet architecture (getting semantic segmented mask of specific image)
Hi Mustajab - Stumbled upon your comment, and I think this paper did what you are talking about - arxiv.org/abs/1708.05227 They used conditional GAN and train a semantic segmentation CNN along with an adversarial network that discriminates segmentation maps coming from the ground truth or from the segmentation network for BraTS 2017 segmentation task More specifically, they used patient-wise ”U-Net” as a generator and ”Markovian GAN” as an discriminator.
Can you make videos on the transformers? Vision Transformer for the classification. The main issue is in understanding the input/output shape, number of patches for different images sizes etc. Thanks in advance.
Sir, Thank you so much. Are you planning to do some tutorials on meat-learning in the future, e.g., learning to learn gradient descent by gradient descent, or learning to learn without gradient descent by gradient descent, and keras implementation?
How can I apply k-fold cross validation in the 195. tutorial(195 - Image classification using XGBoost and VGG16 imagenet as feature extractor). I wish you may help me in this situation. Because the most common problem in practice is overfittig. How can I overcome this in this code Thank you for all your effort Sir.
Sometime in future but definitely not in the next couple of months. Thanks for the suggestion though, I need to find time to put together code that works and then plan videos. Takes time.
Really I got interest in deep learning methods on watching ur tutorials.sir I wish to clarify doubts in my deep learning based work . So can you share your email I'd.
![[Classic] Generative Adversarial Networks (Paper Explained)](http://i.ytimg.com/vi/eyxmSmjmNS0/mqdefault.jpg)
So beautifully explained, so smooth and highly enjoyable! Thanks a lot Dr.
So excited to get to the Code part of GAN, thanks Prof.
Sir, your tutorials make confusing and complicated AI topics to easy and comprehensible concepts for us. Thanks a lot professor
You are most welcome
Thank you!! I'm a data science student and I will start my thesis on this topic next week. Great introduction.
Best of luck!
I cannot thank you enough for sharing your knowledge and preparing and publishing these great tutorials.
Thank you :)
Sreeni sir, great going, these sessions are profoundly useful.
Amazing work, really appreciate your efforts. 🙏🏻 Please keep making such videos.
Great tutorial. Very simple and informative video. I really appreciate your easy and helpful way of explanation. Thanks a million.
Great vid as always. Your videos are great to watch even if I’m not working on the given topic.
If you only watch videos on topics that you relate to, then how do you learn about other topics? I think it is very important for us to learn about various topics so we can find the one that really interests us.
You are very good and very patient teacher. I watch your videos every single day. Thanks for making videos for mere mortals like me! :D
Thankyou sir for this amazing tutorial, very clear explanation, very patient teacher....i really appreciate that. Stay healthy sir
Your videos are genuinely knowledgable sir ...Keep providing with such great contents .
Please provide these slides also if possible
Thank you for the clear explanation! I really appreciate your videos
Please keep watching :)
You deserve a huge round of applause, Thanks for this great content. God bless you:)
Thank you so much for this detailed and easy to follow demonstration! It's a major component of my grad research and you have tied the concepts together so well that it really complements and reinforces my understanding.
You are the man!
Thank you, keep up the good work.
thank you so much you are amazing I have learned so much from you
wonderful explanation 👍🏻👍
Yes sir please make more videos on different GAN architectures.
worth every second. thanks a lot!
thank you for the effort , can i ask you to make an applications for ESRGAN to understand it very well
Thank you for this video!
Great Informative video. Now understand conditional GAN. Thanks #DigitalScreeni
Waiting For StackGan Implementation
Excellent explanation!!!!!!!! Thanks!
Glad it was helpful!
Hi Sreeni,
You were great as always. Do you have Mask RCNN using TF2 in your roadmap or not ?
Thnx a lot for the wonderful explanation
Finally you are back in the game sir 💚💚
Awesome video. Thank you.
Very Good Explained Sir
can we use GANs or CGANs to balance the dataset? Please explain sir
Very helpful video. Can you please tell me that can we perform semantic segmentation using conditional GAN. In this video, you talk about getting real image from semantic segmented image. But can we perform the task we did using UNet architecture (getting semantic segmented mask of specific image)
Hi Mustajab - Stumbled upon your comment, and I think this paper did what you are talking about - arxiv.org/abs/1708.05227
They used conditional GAN and train a semantic segmentation CNN along with an adversarial network that discriminates segmentation maps coming from the ground truth or from the segmentation network for BraTS 2017 segmentation task
More specifically, they used patient-wise ”U-Net” as a generator and ”Markovian GAN” as an discriminator.
Great videos!😊
Excellent Great video sir
very good, thank you
Can you make videos on the transformers? Vision Transformer for the classification. The main issue is in understanding the input/output shape, number of patches for different images sizes etc. Thanks in advance.
thank you sir
Sir, how we can use GAN for noise removal in document images?
Is there a video that can help me with binarization using GAN so i can watch that one
Thanks you Sir ... UOH love ..
Keep continue good luck!
Sir can I use this code for doing RGB to Grayscale images?
Thank you so much :)
Sir, do you have made any video on deep dense GAN? If yes please send me it's lesson number or link... 🙏🏼🙏🏼🙏🏼
Sir, Thank you so much. Are you planning to do some tutorials on meat-learning in the future, e.g., learning to learn gradient descent by gradient descent, or learning to learn without gradient descent by gradient descent, and keras implementation?
Anything is possible and everything is easy with DIgital Sreeni
How to randomize the number of images that are passed in each epoch?
Good information . . .
How to match images for similar products??
How can I apply k-fold cross validation in the 195. tutorial(195 - Image classification using XGBoost and VGG16 imagenet as feature extractor). I wish you may help me in this situation. Because the most common problem in practice is overfittig. How can I overcome this in this code Thank you for all your effort Sir.
thank you
thank you so muchhhhhh
Sir, do you have any video how to make images from text using GANs? I really need some good tutorial on that.
Thanks sir.
Most welcome
Sir, would you please upload tutorials on object detection algorithms like faster RCNN and fast RCNN.
Sometime in future but definitely not in the next couple of months. Thanks for the suggestion though, I need to find time to put together code that works and then plan videos. Takes time.
Thumb up your video though it is busy for something else recently.
subscribed 🤙
Thanks :)
sir can you please share these slides
Really I got interest in deep learning methods on watching ur tutorials.sir I wish to clarify doubts in my deep learning based work . So can you share your email I'd.