Showing 6 open source projects for "python-ldap"

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  • 1
    Deequ

    Deequ

    Deequ is a library built on top of Apache Spark

    ..., and the suggestion of likely constraints automatically for new datasets. It also includes a little domain-specific language called DQDL (Data Quality Definition Language) which allows declarative specification of quality rules. Users typically run Deequ before feeding data downstream (to ML pipelines, analytics, or production systems), enabling early detection and isolation of data errors. There is also a Python wrapper, PyDeequ, for users who prefer working from Python environments.
    Downloads: 6 This Week
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  • 2
    Synapse Machine Learning

    Synapse Machine Learning

    Simple and distributed Machine Learning

    SynapseML (previously MMLSpark) is an open source library to simplify the creation of scalable machine learning pipelines. SynapseML builds on Apache Spark and SparkML to enable new kinds of machine learning, analytics, and model deployment workflows. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with the Open Neural Network Exchange (ONNX), LightGBM, The Cognitive Services, Vowpal Wabbit,...
    Downloads: 2 This Week
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  • 3
    osm4scala

    osm4scala

    Reading OpenStreetMap Pbf files.

    Scala and polyglot Spark library (Scala, PySpark, SparkSQL, ... ) focused on reading OpenStreetMap Pbf files.
    Downloads: 0 This Week
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  • 4
    Assorted projects. General-purpose libraries for Python, C++, Scala, bash, and others. Meta-programming tools. System utilities. UI components. Web APIs. Configuration files. Benchmarks. Programming competition entries. And much more.
    Downloads: 0 This Week
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  • 5
    node2vec

    node2vec

    Learn continuous vector embeddings for nodes in a graph using biased R

    The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction....
    Downloads: 12 This Week
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  • 6

    Waterloo

    Java-based scientific graphics

    Java-based scientific graphics with support for Java, Groovy, MATLAB, Python, the R statistical environment, Scala and SciLab.
    Downloads: 0 This Week
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