@ozachar
As a physicist, I recognize this process as "real space renormalization group" procedure in statistical mechanics. So each layer is equivalent to a renormalization step (a coarse graining). The renormalization flows are then the gradual flow towards a resolution decision of the neural net. It makes the whole "magic" very clear conceptually, and also automatically points the way for less trivial renormalization procedures known in theoretical physics (not just simple real space coarse graining). The clarity of videos like yours is so stimulating! Thanks
@dradic9452
Please make more videos. I've been watching countless neural networks videos and until I saw your two videos I was still lost. You explained it so clearly and concisely. I hope you make more videos.
@user-wv1po8hf8k
Best video series ever, finally answering the real questions I had about HOW they do what they do, not the steps they follow
@IllIl
Dude, your teaching style is absolutely superb! Thank you so much for these. This surpasses any of the explanations I've come across in online courses. Please make more! The way you demystify these concepts is just in a league of its own!
@Number_Cruncher
This was a very cool twist in the end with the rearranged pixels. Thx, for this nice experiment.
@rohithpokala
Bro ,you are real super man.This video gave so many deep insights in just 15 mintues providing so much strong foundation. I can confidently say,this video single handedly throwed 1000's of neural networks videos present on the internet.You raised the bar so high for others to compete.Thanks.
@nadaelnokaly4950
wow!! ur channel is a treasure
@thomassynths
This is by far the best explanation of CNNs I have ever come across. The motivational examples and the presentation are superb.
@nananou1687
This is genuinely one of the best videos I have ever seen! No matter the type of content. You have somehow made one of the most complicated topic, and simply distilled it to this. Brilliant!
@joshlevine4221
3:02 Strictly speaking, there are only a finite number of images for any given image size and pixel depth, so each on can be uniquely described by a single number (and it is even an integer!). These "image numbers" cover a very, very, very wide and sparsely-filled range, but the "image number" still only has a single dimension. Thank you for the great video!
@benjamindilorenzo
The best video on CNN´s. Please make a video about V-Jepa, the proposed SSL Architecture from Yann LeCun. Also it would be nice to have a deeper look at Diffusion Transformers or Diffusion in general. Really really good work man!
@HeadCodeMonkey82
You are a great explainer, and your visuals really helped with my understanding.
@ZetaReticulli
@4:17 Why is it 9^N points required to densely fill N dimensions? Where is 9 being derived from? Is it for the purpose of the example given - or a more general constraint?
@bassemmansour3163
best illustrations in the subject. thank you for your work!
@j.j.maverick9252
another superb summary and visualisation, thank you!
@BenjaminDorra
Thank you for this fascinating video ! It is a very original angle on the effectiveness of CNNs. I have never seen this approach, most articles and videos focus on the reduction in parameters and computation compared with the base MLP or the image compression. Interestingly you don't talk about pooling, a staple of CNNs architectures. Arguably it is mostly for computational efficiency but I have seen a bit of debate on the subject (max pooling being especially polarizing).
@montanacaleb
You are the 3blue1brown of ml
@jcorey333
This is one of the best explanations I've seen! Thanks for making videos
@djenning90
Both this and the transformers video are outstanding. I find your teaching style very interesting to learn from. And the visuals and animations you include are very descriptive and illustrative! I’m your newest fan. Thank you!
@algorithmicsimplicity