Home Projects betty
betty
Python

betty

Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization

by leopard-ai · GitHub
Stars
Forks
License
Created
Last commit
Language
artificial-intelligenceautodiffautomatic-differentiationApache-2.0Python
View on GitHub
In plain words

Use an automatic differentiation tool to improve meta-learning and optimization tasks in your AI projects.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
betty — GitHub preview card
📈 Star history
348347
2026-07-202026-08-31
📈 Track betty

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization

betty has 347 stars on GitHub. It has been forked 28 times. betty is written mainly in Python. It has been in active development since 2022. betty is available under the Apache-2.0 license. Its main topics are artificial-intelligence, autodiff, automatic-differentiation, bilevel-optimization.

Frequently asked questions

What is betty?

Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization

Is betty open source?

betty is an open-source project. It is released under the Apache-2.0 license.

Is betty free?

Yes. betty is free and open source — you can use, modify and self-host it.

What license does betty use?

betty is available under the Apache-2.0 license.

What language is betty written in?

betty is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — betty

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=leopard-ai-betty)](https://olud.ai/project/leopard-ai-betty.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.