Browse free open source Package Managers and projects below. Use the toggles on the left to filter open source Package Managers by OS, license, language, programming language, and project status.

  • Enterprise-grade ITSM, for every business Icon
    Enterprise-grade ITSM, for every business

    Give your IT, operations, and business teams the ability to deliver exceptional services—without the complexity.

    Freshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
    Try it Free
  • Find Hidden Risks in Windows Task Scheduler Icon
    Find Hidden Risks in Windows Task Scheduler

    Free diagnostic script reveals configuration issues, error patterns, and security risks. Instant HTML report.

    Windows Task Scheduler might be hiding critical failures. Download the free JAMS diagnostic tool to uncover problems before they impact production—get a color-coded risk report with clear remediation steps in minutes.
    Download Free Tool
  • 1
    DeepCTR

    DeepCTR

    Package of deep-learning based CTR models

    DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models. You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based techniques have been widely used in CTR prediction task. The data in CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. Since DNN are good at handling dense numerical features,we usually map the sparse categorical features to dense numerical through embedding technique.
    Downloads: 1 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next