Differentiation for Hackers

It begins with a calculus-101 style understanding and gradually extends this to build toy implementations of systems similar to PyTorch and TensorFlow. I have tried to clarify the relationships between every kind of differentiation I can think of – including forward and reverse, symbolic, numeric, tracing and source transformation. Where typical real-word ADs are mired in implementation details, these implementations are designed to be coherent enough that the real, fundamental differences – of which there are surprisingly few – become obvious.

Source: github.com