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