3 projects for "logistic" with 2 filters applied:

  • MongoDB Atlas runs apps anywhere Icon
    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

    MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
    Start Free
  • Add Two Lines of Code. Get Full APM. Icon
    Add Two Lines of Code. Get Full APM.

    AppSignal installs in minutes and auto-configures dashboards, alerts, and error tracking.

    Works out of the box for Rails, Django, Express, Phoenix, and more. Monitoring exceptions and performance in no time.
    Start Free
  • 1
    Moquette Project

    Moquette Project

    Java MQTT lightweight broker

    JVM lightweight MQTT broker for the Internet of Things. Simply embeddable in your IoT projects. Moquette aims to be a MQTT compliant broker. The broker supports QoS 0, QoS 1 and QoS 2. Its designed to be evented, uses Netty for the protocol encoding and decoding part.
    Downloads: 1 This Week
    Last Update:
    See Project
  • 2
    GLM.jl

    GLM.jl

    Generalized linear models in Julia

    GLM.jl is a Julia package for fitting linear and generalized linear models (GLMs) with a syntax and functionality familiar to users of R or other statistical environments. It is part of the JuliaStats ecosystem and is tightly integrated with StatsModels.jl for formula handling, and Distributions.jl for specifying error families. The package supports modeling through both formula-based (e.g. @formula) and matrix-based interfaces, allowing both high-level convenience and low-level control....
    Downloads: 0 This Week
    Last Update:
    See Project
  • 3
    benchm-ml

    benchm-ml

    A benchmark of commonly used open source implementations

    ...It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. The benchmarks cover algorithms like logistic regression, random forest, gradient boosting, and deep neural networks, and they compare across toolkits such as scikit-learn, R packages, xgboost, H2O, Spark MLlib, etc. The repository is structured in logical folders, each corresponding to algorithm categories.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next
MongoDB Logo MongoDB