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Exploring AI optimization

This library aims to become a curated list of awesome material (cool papers, tutorials, courses, etc.) on optimization techniques to make artificial intelligence faster and more efficient 🚀

Among the best acceleration techniques for deep learning are:

• Quantization [Overview, Resources]
• Pruning [Overview, Resources]
• Distillation [Overview, Resources]
• Deep learning compilers [Overview, Resources]

And many others. Do you have any idea on material to expand the list? We are welcoming contributions! And don't forget to leave a star if you found this library insightful ⭐

Contribute

We welcome support from the open-source community in any form 🥰

We have recently published this library. We will continue to update and upgrade it with great material on how to accelerate artificial intelligence models and make them ever more efficient. We also hope you will also learn with us and help us keep the library populated with the best quality content.

Open an issue or drop a message in the community channel to

• Add topics you would like to be covered
• Ask any questions
• Report a typo
• Propose to include your research work

Fork the library, make the changes, and send us a pull request in case you want to

• Write new content on AI optimization
• Correct or improve some sections

Currently, the main contributor to the library is Pier, and the library would not have been possible without Diego's interesting insights and topic suggestions and support from the community.

The community for AI acceleration

Do you want to meet the contributors of this libraries and other developers who share the vision of an superfast and sustainable artificial intelligence? Join the community for AI acceleration on Discord!

Join the community • Contribute to the library