3 ms·
Animated AI
- krackers 10mo agoYou should add dilated conv and conv_transpose to the list.
- wwarner 10mo agoI feel like these are helpful, and I think the calculus oriented visualizations of convex surfaces and gradient descent help a lot as well.
- jerpint 10mo agoNice! I made my own version of this many years ago, with a very basic manim animation https://www.jerpint.io/blog/2021-03-18-cnn-cheatsheet/ https://www.jerpint.io/blog/2021-03-18-cnn-cheatsheet/
- mnkv 10mo agoNice work. A while back, I learned convolutions using similar animations by Vincent Dumoulin and Francesco Visin's gifs https://github.com/vdumoulin/conv_arithmetic https://github.com/vdumoulin/conv_arithmetic
- _giorgio_ 10mo agoVery good arxiv paper, I wish there where some updates on that.
- amkharg26 10mo agoThis is a fantastic educational resource! Visual animations like these make understanding complex ML concepts so much more intuitive than just reading equations. The neural network visualization is particularly well done - seeing the forward and backward passes in action helps build the right mental model. Would be great to see more visualizations covering transformer architectures and attention mechanisms, which are often harder to grasp. For anyone building educational tools or internal documentation for ML teams, this approach of animated explanations is really effective for knowledge transfer.
- throwaway2027 10mo agoI don't think these are useful at all. If you implement a simple network that approximates 1D functions like sin or learn how image blurring works with kernels and then move into ML/AI that gave me a much better understanding.
- barrenko 10mo agoYup, I'd say you learn more by doing math by hand (shouldn't be that surprising).
- nosianu 10mo agoSo... I remember math including doing quite a bit of geometry by hand and with real tools, at least initially. "Math" is not just the symbol stuff written with a pencil, or with a keyboard. The mechanical analog computers of old (e.g. https://youtu.be/IgF3OX8nT0w https://youtu.be/IgF3OX8nT0w, or https://youtu.be/s1i-dnAH9Y4 https://youtu.be/s1i-dnAH9Y4) are examples too that math is more than symbol manipulation.
- nobodywillobsrv 10mo agoThank you for saying this. I often find this "glib" explains of ML stuff very frustrating as a human coming from an Applied Math background. It just makes me feel a bit crazy and alone to see what appears to be a certain kind of person saying "gosh" at various "explanations" when I just don't get it. Obviously this is beautiful as art but it would also be useful to understand how exactly these visualizations are useful to people who think they are. Useful to me means you gain a new ability to extrapolate in task space (aka "understanding").
- patresh 10mo agoThey're likely of limited use for someone looking for introductory material to ML, but for someone having done some computer vision and used various types convolution layers, it can be useful to see a summary with visualizations.
- noduerme 10mo agoIdk, it's fun. 20 years ago I made a cubic neural model in Flash that actually lit up cubes depending on how much they were being accessed. This was a case of binding logic way too tightly to display code, but it was a cool experiment.
- sapphirebreeze 10mo ago[dead]
- jaredwilber 10mo agoYears back I worked on some animated ML articles, my favorites being: https://mlu-explain.github.io/neural-networks/ https://mlu-explain.github.io/neural-networks/ and https://mlu-explain.github.io/decision-tree/ https://mlu-explain.github.io/decision-tree/
- SpaceManNabs 10mo agonot deep learning but this oldie is a goodie too (since we are sharing favorites): https://narrative-flow.github.io/exploratory-study-2/ https://narrative-flow.github.io/exploratory-study-2/ I originally had it saved as [[ https://www.r2d3.us/visual-intro-to-machine-learning-part-1/ https://www.r2d3.us/visual-intro-to-machine-learning-part-1/ ]] but it seems that link is gone?
- sujayk_33 10mo agoI worked on something similar but specifically for transformer architecture: https://transformer.sujayk.me/ https://transformer.sujayk.me/
- fuzzy_lumpkins 10mo agoamazing resource!
- jlebar 10mo agoShameless plug for my writeup about convolutions: https://jlebar.com/2023/9/11/convolutions.html https://jlebar.com/2023/9/11/convolutions.html
- diginova 10mo agohere is the github link for anyone wanting to star the repo https://github.com/animatedai/animatedai https://github.com/animatedai/animatedai
- mg 10mo agoIs there an error in the first video at 00:25? https://www.youtube.com/watch?v=eMXuk97NeSI&t=25 https://www.youtube.com/watch?v=eMXuk97NeSI&t=25 It says the input has 3 dimensions, two spatial dimensions and one feature dimension. So it would be a 2D grid of numbers. Like a grayscale photo. But at 00:38 it shows the numbers and it looks like each of the blocks positioned in 3D space holds a floating-point value. Which would make it a 4-dimensional input.
- kristopolous 10mo agoalso look at https://poloclub.github.io/transformer-explainer/ https://poloclub.github.io/transformer-explainer/
- johnnyfived 10mo agoNow this one actually looks usable
- kristopolous 10mo agoI've been wanting to build a really small transformer based system but for some reason I only remember that I want to do that at like 3am and my brain has turned off.
- josephant 10mo agoThis looks great for explaining transformer!!