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* [Netron](https://github.com/lutzroeder/netron) - Visualizer for Neural Network, Deep Learning, and Machine Learning Models
* [Scikit Learn](https://github.com/scikit-learn/scikit-learn) - Machine Learning Library for Python
* [Kobra](https://kobra.dev/) - Visual Programming Language for Machine Learning
* [DVC](https://dvc.org/) - Machine Learning Version Control
* [DialoGPT](https://github.com/microsoft/DialoGPT) - Dialogue Machine Learning Model
* [MMDeploy](https://github.com/open-mmlab/mmdeploy) - Deep Learning Model Deployment Toolset
* [DeepSpeed](https://github.com/microsoft/DeepSpeed) - Deep Learning Optimization Library
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* [100-Days-Of-ML-Code](https://github.com/Avik-Jain/100-Days-Of-ML-Code) or [Practical Deep Learning](https://course.fast.ai/) - Machine Learning Coding Lessons
* [Nixified](https://nixified.ai/) - Nix Flake for AI Projects
* [Approaching (Almost) Any Machine Learning Problem](https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf) - Machine Learning Problem Solving Book
* [MetaAcademy](https://metacademy.org/) - Machine Learning Guides
* [MetaAcademy](https://metacademy.org/) or [HF Learn](https://huggingface.co/learn) - Machine Learning Guides
* [ML-Youtube-Courses](https://github.com/dair-ai/ML-YouTube-Courses) - Machine Learning Courses on YouTube
* [ML Course Notes](https://github.com/dair-ai/ML-Course-Notes) - Machine Learning Course Notes
* [100-Days-Of-ML-Code](https://github.com/Avik-Jain/100-Days-Of-ML-Code) or [Practical Deep Learning](https://course.fast.ai/) - Machine Learning Coding Lessons