1/2/2026 at 1:05:32 AM
Thanks for sharing! It's inspiring to see more people "reinventing for insight" in the age of AI. This reminds me of my similar previous project a year ago when I built an entire PyTorch-style machine learning library [1] from scratch, using nothing but Python and NumPy. I started with a tiny autograd engine, then gradually created layer modules, optimizers, data loaders etc... I simply wanted to learn machine learning from first principles. Along the way I attempted to reproduce classical convnets [2] all the way to a toy GPT-2 [3] using the library I built. It definitely helped me understand how machine learning worked underneath the hood without all the fancy abstractions that PyTorch/TensorFlow provides. I eventually wrote a blog post [4] of this journey.[1] https://github.com/workofart/ml-by-hand
[2] https://github.com/workofart/ml-by-hand/blob/main/examples/c...
[3] https://github.com/workofart/ml-by-hand/blob/main/examples/g...
by megadragon9
1/2/2026 at 2:11:20 AM
During my Bachelor's, I wrote a small "immutable" algebraic machine learning library based on just NumPy. This made it easy to play around with combining weights by simply summing two networks by whatever operations are supported on normal NumPy arrays.... turns out, this is only useful in some very specific scenarios, and it's probably not worth the extreme memory overhead.
by RestartKernel