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* ⭐ **[Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)**, [Caffee](https://caffe.berkeleyvision.org/) / [Git](https://github.com/BVLC/caffe), [SuperAGI](https://github.com/TransformerOptimus/SuperAGI) or [OpenNN](https://www.opennn.net/) / [Git](https://github.com/Artelnics/OpenNN) - Machine Learning Frameworks
* ⭐ **[awesome-list-of-awesomes](https://github.com/Nachimak28/awesome-list-of-awesomes)**, [awesome-marketing-datascience](https://github.com/underlines/awesome-marketing-datascience/), [awesome-decentralized-llm](https://github.com/imaurer/awesome-decentralized-llm), [CyberCowboy](https://local-llm.cybercowboy.de) or [ML_Resources](https://github.com/tunguz/ML_Resources) - Machine Learning Resources
* [TensorFlow](https://www.tensorflow.org/) - Create Machine Learning Models
* [Catalyzex](https://www.catalyzex.com/) or [PapersWithCode](https://paperswithcode.com/) - Search Machine Learning Models / Papers
* [awesome-google-colab](https://github.com/firmai/awesome-google-colab) - Machine Learning Colabs
* [OpenAIPlayground](https://github.com/nat/openplayground)
* [DVC](https://dvc.org/) - Machine Learning Version Control
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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
* [Awesome Machine Learning Art](https://github.com/vibertthio/awesome-machine-learning-art) - Machine Learning Art Resource Index
* [Catalyzex](https://www.catalyzex.com/), [Feedly AI](https://feedly.com/ai) or [PapersWithCode](https://paperswithcode.com/) - Search Machine Learning Models / Papers
* [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
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* [Netron](https://github.com/lutzroeder/netron) - Visualizer for Neural Network, Deep Learning, and Machine Learning Models
* [Cyberbotics](https://cyberbotics.com/) - Robot Simulator
* [Dataset Card for Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) or [awesome-instruction-dataset](https://github.com/yaodongC/awesome-instruction-dataset) - Language Model Training Datasets
* [PromptPapers](https://github.com/thunlp/PromptPapers) - Pre-Trained Language Model Tuning Papers
* [MLC LLM](https://github.com/mlc-ai/mlc-llm) - Native Language Model Deployment
* [Shap](https://github.com/slundberg/shap) - Language Model Response Analyzer
* [Langchain](https://github.com/hwchase17/langchain) - Build Apps via LLM
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* [minitorch](https://github.com/minitorch/minitorch) - Machine Learning Engineering Course Code
* [Approaching (Almost) Any Machine Learning Problem](https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf) - Machine Learning Problem Solving Book
* [ML-Youtube-Courses](https://github.com/dair-ai/ML-YouTube-Courses) - Machine Learning Courses on YouTube
* [Catalyzex](https://www.catalyzex.com/), [Feedly AI](https://feedly.com/ai), [PapersWithCode](https://paperswithcode.com/), [AI Reading List](https://docs.google.com/document/d/1bEQM1W-1fzSVWNbS4ne5PopB2b7j8zD4Jc3nm4rbK-U/), [AI RND](https://www.ai-rnd.com/) or [Daily Papers](https://huggingface.co/papers) - AI Research Papers
* [PromptPapers](https://github.com/thunlp/PromptPapers) - Pre-Trained Language Model Tuning Papers