Showing 5 open source projects for "research"

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  • 1
    PyTorch Lightning

    PyTorch Lightning

    The lightweight PyTorch wrapper for high-performance AI research

    Scale your models, not your boilerplate with PyTorch Lightning! PyTorch Lightning is the ultimate PyTorch research framework that allows you to focus on the research while it takes care of everything else. It's designed to decouple the science from the engineering in your PyTorch code, simplifying complex network coding and giving you maximum flexibility. PyTorch Lightning can be used for just about any type of research, and was built for the fast inference needed in AI research and production. ...
    Downloads: 0 This Week
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  • 2
    Kodus

    Kodus

    AI code reviews, just like your senior dev would do

    ...It provides a structured set of tools and abstractions that help teams design agent behaviors, orchestrate data pipelines, optimize inference, and integrate AI capabilities with applications or services. The platform often includes model management, scalable training workflows, and orchestration patterns that help teams move from research or prototypes to production-ready AI deployments. Through configurable pipelines and a focus on modularity, it supports experimentation while maintaining reproducibility and performance. Its tooling is typically designed to handle real-world imperatives like logging, monitoring, versioning, and hooking into operational infrastructure.
    Downloads: 6 This Week
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  • 3
    Bell Log File Reader

    Bell Log File Reader

    This software like a Text Log File Reader.

    This software like a Text Log File Reader.
    Downloads: 0 This Week
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  • 4
    Reviewing memory allocation and data structures of an extant Sourceforge project unix-named "simupop". A new development version of the extant project is established and the ultimate goal is to "rev up" the old, hence the project name.
    Downloads: 0 This Week
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  • 5
    Medical Datasets (In a text file, with space separated values) can be loaded to the system. By choosing either one of the two classifiers, Neural network or Decision Tree, the system can be trained and evaluated.
    Downloads: 0 This Week
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