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C Big Data Tools for Mac

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Browse free open source C Big Data Tools for Mac and projects below. Use the toggles on the left to filter open source C Big Data Tools for Mac by OS, license, language, programming language, and project status.

  • Gen AI apps are built with MongoDB Atlas Icon
    Gen AI apps are built with MongoDB Atlas

    The database for AI-powered applications.

    MongoDB Atlas is the developer-friendly database used to build, scale, and run gen AI and LLM-powered apps—without needing a separate vector database. Atlas offers built-in vector search, global availability across 115+ regions, and flexible document modeling. Start building AI apps faster, all in one place.
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  • Contractor Foreman is the most affordable all-in-one construction management software for contractors and is trusted by contractors in more than 75 countries. Icon
    Contractor Foreman is the most affordable all-in-one construction management software for contractors and is trusted by contractors in more than 75 countries.

    For Residential, Commercial and Public Works Contractors

    Starting at $49/m for the WHOLE company, Contractor Foreman is the most affordable all-in-one construction management system for contractors. Our customers in 75+ countries and industry awards back it up. And it's all backed by a 100 day guarantee.
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  • 1
    FastoRedis

    FastoRedis

    Cross-platform open source Redis DB management tool

    FastoRedis (fork of FastoNoSQL) — is a cross-platform open source Redis management tool (i.e. Admin GUI). It put the same engine that powers Redis's redis-cli shell. Everything you can write in redis-cli shell — you can write in FastoRedis! Our program works on the most amount of Linux systems, also on Windows, Mac OS X, FreeBSD and Android platforms, on desktops and embedded devices.
    Downloads: 0 This Week
    Last Update:
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  • 2

    Random Bits Forest

    RBF: a Strong Classifier/Regressor for Big Data

    We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. In conclusion, RBF is a novel framework that performs strongly especially on data with large size.
    Downloads: 0 This Week
    Last Update:
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