rHXN - Animated AI

jerpint - 17h 19m ago

Nice! 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/

jaredwilber - 16h 1m ago

Years back I worked on some animated ML articles, my favorites being: https://mlu-explain.github.io/neural-networks/ and https://mlu-explain.github.io/decision-tree/

SpaceManNabs - 15h 44m ago

not deep learning but this oldie is a goodie too (since we are sharing favorites): 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/ ]] but it seems that link is gone?

throwaway2027 - 16h 33m ago

I 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.

socalgal2 - 1h 57m ago

Agree. They didn't seem to convey any info what-so-ever, pretty as they were

noduerme - 9h 47m ago

Idk, 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.

amelius - 4h 14m ago

Yes, especially if you ask someone why one is better than the other in a certain configuration.

barrenko - 16h 3m ago

Yup, I'd say you learn more by doing math by hand (shouldn't be that surprising).

nosianu - 11h 16m ago

So... 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, or https://youtu.be/s1i-dnAH9Y4) are examples too that math is more than symbol manipulation.

patresh - 11h 52m ago

They'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.

nobodywillobsrv - 12h 10m ago

Thank 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").

j45 - 7h 30m ago

Learning first principles of something are always useful for beginners.

Everyone is a beginner at something.

sujayk_33 - 15h 57m ago

I worked on something similar but specifically for transformer architecture: https://transformer.sujayk.me/

yu3zhou4 - 14h 17m ago

On Safari mobile it shows a modal that can’t be scrolled nor closed

sujayk_33 - 9h 15m ago

Yeah, it's not mobile-friendly. didn't get a chance to look into it

mg - 12h 51s ago

Is there an error in the first video at 00:25?

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.

mnkv - 17h 10m ago

Nice work. A while back, I learned convolutions using similar animations by Vincent Dumoulin and Francesco Visin's gifs

https://github.com/vdumoulin/conv_arithmetic

wwarner - 17h 36m ago

I feel like these are helpful, and I think the calculus oriented visualizations of convex surfaces and gradient descent help a lot as well.

kristopolous - 11h 38m ago

also look at https://poloclub.github.io/transformer-explainer/

jlebar - 14h 11m ago

Shameless plug for my writeup about convolutions: https://jlebar.com/2023/9/11/convolutions.html

diginova - 13h 20m ago

here is the github link for anyone wanting to star the repo https://github.com/animatedai/animatedai

amkharg26 - 17h 4m ago

This 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.

krackers - 12h 49m ago

You should add dilated conv and conv_transpose to the list.

fuzzy_lumpkins - 14h 25m ago

amazing resource!

sapphirebreeze - 16h 8m ago

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