Showing 7 open source projects for "scientists"

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  • 1
    InterpretML

    InterpretML

    Fit interpretable models. Explain blackbox machine learning

    In the beginning, machines learned in darkness, and data scientists struggled in the void to explain them. InterpretML is an open-source package that incorporates state-of-the-art machine-learning interpretability techniques under one roof. With this package, you can train interpretable glass box models and explain black box systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions.
    Downloads: 0 This Week
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  • 2
    cuML

    cuML

    RAPIDS Machine Learning Library

    cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU...
    Downloads: 0 This Week
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  • 3
    OpenMLDB

    OpenMLDB

    OpenMLDB is an open-source machine learning database

    ... and inference. Real-time features are essential for many machine learning applications, such as real-time personalized recommendations and risk analytics. However, a feature engineering script developed by data scientists (Python scripts in most cases) cannot be directly deployed into production for online inference because it usually cannot meet the engineering requirements, such as low latency, high throughput and high availability.
    Downloads: 0 This Week
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  • 4
    xLearn

    xLearn

    High performance, easy-to-use, and scalable machine learning (ML)

    xLearn is a high-performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM), all of which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data. Many real-world datasets deal with high dimensional sparse feature vectors like a recommendation system where the number of categories and...
    Downloads: 0 This Week
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  • 5
    ISS SPHERES

    ISS SPHERES

    SPHERES Facility software for scientists

    Downloads: 0 This Week
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  • 6
    An open source optical flow algorithm framework for scientists and engineers alike.
    Downloads: 1 This Week
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  • 7
    RobotCub
    iCub is an open platform for scientists interested in investigating issues in embodied cognition. Here you find all the software and hardware documentation.
    Downloads: 0 This Week
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