Comment by throwaway2027
15 hours 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.
Agree. They didn't seem to convey any info what-so-ever, pretty as they were
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.
Yes, especially if you ask someone why one is better than the other in a certain configuration.
Yup, I'd say you learn more by doing math by hand (shouldn't be that surprising).
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.
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.
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").
Learning first principles of something are always useful for beginners.
Everyone is a beginner at something.