Showing 3 open source projects for "virtual windows machine"

View related business solutions
  • 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
  • Earn up to 16% annual interest with Nexo. Icon
    Earn up to 16% annual interest with Nexo.

    Access competitive interest rates on your digital assets.

    Generate interest, borrow against your crypto, and trade a range of cryptocurrencies — all in one platform. Geographic restrictions, eligibility, and terms apply.
    Get started with Nexo.
  • 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,...
    Downloads: 0 This Week
    Last Update:
    See Project
  • 2
    scalajs-react

    scalajs-react

    Facebook's React on Scala.JS

    Scala.js React wraps Facebook React for Scala.js with a strong emphasis on type safety and functional idioms. It provides a typed virtual DOM interface, reusable components, hooks, and utilities for routing, testing, SSR, and performance profiling, all aligned with Cats, Cats Effect, and Monocle ecosystems.
    Downloads: 5 This Week
    Last Update:
    See Project
  • 3
    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: 4 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next
MongoDB Logo MongoDB