Wow this is such a treasure to have freely available and I am so thankful that you put this out for the community. Many many thanks good sir, your work towards educating the masses about AI and Machine Learning is so very much appreciated. 🎉❤
I am so glad you took your time to correct the math mistake! Great work! And thank you for your mission of teaching us new findings in AI and deep learning 🙏
At 1:13:20 aren't we supposed to add derivatives to pixel values since we are maximizing P? Unless, since P is binary and it looks like a classification problem, we are going to get negative logits, then deducting seems ok (not touching the sign). Great course!
That math correction was very essential to me. Coming from a mechanical background, I knew something was off, but then thought I didn't know enough about DL to figure out what it is, and that I was on the wrong. With the math correction, it clicked, and was something I knew all along.Thanks.
Amazing video and really easy to follow up with the topics. Its neat how different research is coming together to build something more efficient and promising. So future of AI is how models fit together?
Thank you for great explanation .. I just wanted to know ans to 'what is U-net?' could not understand where is it used in whole process ? I mean what I could not get is what is the difference between VAE (Autoencoder) and an Unet
During *training*, you pass an actual image into the VAE ENcoder (to reduce the amount of data you have to deal with), which then passes the latent it produces on to the UNet, which does the learning involving noising/denoising the latent. During *inference* ("generating"), the UNet (after a lot of other stuff happens :D) passes out a denoised latent to the VAE DEcoder, which then produces an actual image
at 1:56:50 I'm having hard time understanding the cost function. I think we need to maximise ( Green Summation - Red Summation ) , for that reason we can't call it a cost function because cost functions are usually minimised. Please correct me If I'm wrong
since we want to increase the probability of our image being a digit, we should "add" and not "substract" the grad of probability wrt to img. Is this right? or am I missing something ?
Very an excellent course, I would like to ask a question that the noise N(0,0.1) is added equally to each pixel or to the whole image at 1:21:51? These two are equivalent？
Different per pixel! You’re drawing a vector of random noise samples then reshaping it into an image, so you get many values but all from a dist with low variance. The python random numbers let you sample in the shape of an image directly, so you don’t need to manually reshape. But that’s just for convenience
I've been working on/with diffusion models (and before that VQGANs!) for years now, so I'm pretty familiar (from the end-user/theoretical POV, not so much the math/code side heh) with samplers/schedulers - this is the first time I've conceived of them as optimizers, and that seems like a *really* fertile area to research. Have you (or anyone else, for that matter) made any progress in this direction? It's (not too surprisingly) VERY hard to prompt today's search engines to find anything to do with "denoise|diffusion|schedule|sample|optimize" and NOT come up with dozens of either HuggingFace docs pages, or pages w.r.t. Stable DIffusion ROFL
52:12 - The upside down triangle is "nabla", "del" is the squiggly d that goes before each partial derivative. Also, I'm jealous of people who started calculus in high school lol
1:04:12 wait you still mixed them up 😅 At this rate with your following, you're going to speak it into existence though lol. Math notation ultimately is whatever everyone agrees upon, so I could see it being possible.
I was wondering if I could give some suggestions, you spent 20 mins to explain the course materials and different people. Why not start from main fun play first, then later introduce more about the course materials. People will lose interest to listen to 20 mins course materials.
