mattjj/autodidact resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1024 stars · 108 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A small, tutorial-style reimplementation of the Autograd automatic differentiation library, written for learning how reverse-mode autodiff works. It provides the same grad-based API as Autograd over a thinly-wrapped NumPy.

Use cases

• learn how automatic differentiation works internally
• understand reverse-mode gradient computation
• compute gradients and higher-order derivatives of numpy functions
• study the source of a minimal autograd system
• teach a course on differentiable programming

When to choose

• you want readable source code explaining how autodiff is implemented
• you are teaching or learning the internals of tools like Autograd or JAX
• you need a tiny dependency-free autodiff to experiment with

When to avoid

• you need a production-grade or performant autodiff system
• you want GPU acceleration or large-scale deep learning support
• you need an actively maintained library - use JAX or PyTorch instead

Facets

learning-resource · maturity maintenance

machine-learning math developer-tools machine-learning education python autodiff automatic-differentiation autograd pedagogical jupyter-notebook gradients algorithms

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