Comment by megadragon9

I built a machine learning library [1] (similar to PyTorch's API) entirely from scratch using only Python and NumPy. It was inspired by Andrej Karpathy's Micrograd project [2]. I slowly added more functionality and evolved it into a fully functional ML library that can build and train classical CNNs [3] to even a toy GPT-2 [4].

I wanted to understand how models learn, like literally bridging the gap between mathematical formulas and high-level API calls. I feel like, as a beginner in machine learning, it's important to strip away the abstractions and understand how these libraries work from the ground up before leveraging these "high-level" libraries such as PyTorch and Tensorflow. Oh I also wrote a blog post [5] on the journey.

[1] https://github.com/workofart/ml-by-hand

[2] https://github.com/karpathy/micrograd

[3] https://github.com/workofart/ml-by-hand/blob/main/examples/c...

[4] https://github.com/workofart/ml-by-hand/blob/main/examples/g...

[5] https://www.henrypan.com/blog/2025-02-06-ml-by-hand/