The Rosetta Stone of Backpropagation

What it does

This repository collects community ports of the Neural Networks from Scratch book and video series into languages beyond Python. Each contribution maps a specific tutorial part to a single file in another language, creating a reference for how the curriculum’s code looks outside the Python ecosystem.

The interesting bit

The real value is anthropological: you can watch the same educational neural net evolve across whatever languages contributors submit. It is less a framework and more a polyglot reference for the book’s fundamentals, showing how different languages handle the same tutorial logic.

Key highlights

• Content is indexed by video/book part number, so you can jump straight to the chapter you’re studying.
• Each submission must be a single file, which keeps the repository maintainable and makes comparisons trivial.
• Maintainers prioritize PRs from existing language contributors and first-comers, so the codebase accretes rather than fragments.
• The Python versions remain the canonical source of truth; other languages follow their lead.

Caveats

• The README does not specify which languages are currently represented or how complete the coverage is, so you may find your language of interest is only partially ported.
• Because it is volunteer-driven and tied to an external curriculum, there is no guarantee that non-Python implementations stay synchronized with future Python updates.

Verdict

Worth bookmarking if you are learning neural networks and want to see the curriculum translated into your preferred language’s syntax. Skip it if you need a maintained, production-ready deep-learning library.