Build a Deep Learning Library (zekcrates.quarto.pub)

This is cool! This summer I made something similar but in C++. The goal was to build an entire LLM, but I only got to neural networks. GitHub repo here: https://github.com/amitav-krishna/llm-from-scratch. I have a few blogs on this project on my website (https://amitav.net/building-lists.html, https://amitav.net/building-vectors.html, https://amitav.net/building-matrices.html (incomplete)). I hope to finish that series eventually, but some other projects have stolen the spotlight! It probably would have made more sense to write it in Python because I had no C++ experience.

It's alright, but a C version would be even better to fully grasp the implementation details of tensors etc. Shelling out to numpy isn't particularly exciting.

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Did something similar a while back [1], best way to learn neural nets and backprop. Just using Numpy also makes sure you get the math right without having to deal with higher level frameworks or c++ libraries.

[1] https://github.com/santinic/claudioflow

Isn't this what Karpathy does as well in the Zero to Hero lecture series on YT? I am sure this is great as well!

This is good. Its well positioned for software engineers to understand DL stuff beyond the frameworks.

Perhaps obvious to some, but this does not seem to be about learning in the traditional sense, nor a library in the book sense, unfortunately.

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...

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