Welcome to the GitHup repo for ML+X Nexus! Nexus is the ML+X community’s centralized hub for sharing machine learning (ML) resources. Visit the ML+X website to learn more about the community, and join the ML+X google group to stay informed on upcoming community events!
This platform serves as a centralized hub for individuals interested in advancing their knowledge and skills in machine learning (ML) and related fields (X). Whether you're a student, researcher, or industry professional, you'll find a wealth of curated resources tailored to various experience levels and areas of interest. Visit the ML+X Nexus website to begin exploring ML resources.
Any content (original or external) that can help make the practice of ML more connected, accessible, efficient, and reproducible is welcome on the Nexus platform! This includes, but is not limited to:
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🧠 Educational materials: Explore a library of educational materials (workshops, guides, books, videos, etc.) covering a wide range of ML-related topics, tools, and workflows, from foundational concepts to advanced techniques. These materials offer clear explanations, practical examples, and actionable insights to help you navigate the complexities of ML with confidence.
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🧬 Applications & stories: Discover a curated collection of blogs, papers, and talks which dive into real-world ML applications and lessons learned by practitioners. This section also includes exploratory data analysis (EDA) case studies, which demonstrate the technical and domain knowledge needed to explore data from various fields.
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🛠 Models, code, and more: Learn about popular pretrained & foundation models, useful scripts, and datasets that you can leverage for your next ML project. Learn about their features, how to use them effectively, and see examples of them in action.
We welcome contributions from members of the ML+X community! Whether you'd like to share your own guides, best practices, or starter notebooks, or collaborate with others to refine existing resources, your contributions are highly valued and appreciated.
To contribute, fork this repository, make your changes, and submit a pull request. Please ensure that your contributions adhere to our guidelines and maintain the high standards of quality and relevance upheld by the ML+X community. For further guidance and starter templates, please check the How to Contribute? guide.
Join our Slack (#ml-community) to engage with fellow members, participate in discussions, and stay updated on the latest developments within the ML+X community.
Happy learning and happy collaborating!