vugrad is a miniature autodiff system. Think Pytorch or Tensorflow, but designed to be easy to understand (and not suitable for production).
There are many packages like this (see below for a list). Here are vugrad's specifics:
- Built on numpy, which is the only requirement.
- Tensor-valued: all operations are on numpy arrays.
- Eager execution: computation graphs are built on the fly.
vugrad was built for the Deep Learning course at the Vrije Universiteit Amsterdam.
The MNIST loader was taken from this repository by Hyeonseok Jung.
The first package to do something like this was probably micrograd By Andrej Karpathy, followed quickly by minigrad by Kenny Song. These are both scalar-valued, which mean they don't illustrate how tensors are handled.
On the other end of the spectrum, we have minitorch which is a full reimplementation of the Pytorch API in educational code. That means they don't defer to numpy for the tensor implementation, like we do, but they build that from scratch as well.