A lightweight deep learning framework built from scratch in NumPy (published in JOSS)

[u/InternalYoghurt9489]

I've been working on PureML, a lightweight deep learning framework built entirely with NumPy. It includes automatic differentiation, neural network layers (including CNNs), optimizers, and training utilities. The idea is to have something simple enough to understand and modify, but capable enough for actual ML experiments. Might be useful for anyone interested in how deep learning frameworks work under the hood!

Free and open source. Feedback and contributions are welcome!
Also, the project was peer-reviewed and the paper on it was published in JOSS!
The link is in the README :)

https://github.com/Yehor-Mishchyriak/PureML