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Comments (21)

• jerpint
  Nice! I made my own version of this many years ago, with a very basic manim animationhttps://www.jerpint.io/blog/2021-03-18-cnn-cheatsheet/
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  jaredwilber
  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/
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  throwaway2027
  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.
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  sujayk_33
  I worked on something similar but specifically for transformer architecture: https://transformer.sujayk.me/
• mg
  Is there an error in the first video at 00:25?https://www.youtube.com/watch?v=eMXuk97NeSI&t=25It 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
  Nice work. A while back, I learned convolutions using similar animations by Vincent Dumoulin and Francesco Visin's gifshttps://github.com/vdumoulin/conv_arithmetic
• jlebar
  Shameless plug for my writeup about convolutions: https://jlebar.com/2023/9/11/convolutions.html
• wwarner
  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
  also look at https://poloclub.github.io/transformer-explainer/
• diginova
  here is the github link for anyone wanting to star the repo https://github.com/animatedai/animatedai
• amkharg26
  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
  You should add dilated conv and conv_transpose to the list.
• fuzzy_lumpkins
  amazing resource!
• sapphirebreeze
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