Showing 5 open source projects for "java machine learning library"

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  • 1
    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, and OpenCV. ...
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  • 2
    Aerosolve

    Aerosolve

    A machine learning package built for humans

    Aerosolve is an open-source machine learning library developed by Airbnb, designed for interpretable and human-friendly modeling. Built around sparse, human-intuitive features (like geography, pricing), it supports feature quantization, interaction specification, and rule-based priors—enabling domain experts to contribute directly to model behavior.
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  • 3
    TransmogrifAI

    TransmogrifAI

    TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library

    TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Apache Spark. It was developed with a focus on accelerating machine learning developer productivity through machine learning automation, and an API that enforces compile-time type-safety, modularity, and reuse. Through automation, it achieves accuracies close to hand-tuned models with almost 100x reduction in time.
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  • 4

    Delayed Response Network

    Neural network based on signal delays.

    An artificial neural network, currently specialized to save a specific bit pattern, mainly by changing the signal propagation delays in links. More features, variables and algorithms will be added in time.
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  • 5
    PhiWeave is a machine learning library for structured prediction via factor graphs. It is part of an ongoing effort to implement and improve on the current state-of-the-art in inference and parameter estimation for graphical models.
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