Showing 2 open source projects for "python data analysis"

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  • SoftCo: Enterprise Invoice and P2P Automation Software Icon
    SoftCo: Enterprise Invoice and P2P Automation Software

    For companies that process over 20,000 invoices per year

    SoftCo Accounts Payable Automation processes all PO and non-PO supplier invoices electronically from capture and matching through to invoice approval and query management. SoftCoAP delivers unparalleled touchless automation by embedding AI across matching, coding, routing, and exception handling to minimize the number of supplier invoices requiring manual intervention. The result is 89% processing savings, supported by a context-aware AI Assistant that helps users understand exceptions, answer questions, and take the right action faster.
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  • Data management solutions for confident marketing Icon
    Data management solutions for confident marketing

    For companies wanting a complete Data Management solution that is native to Salesforce

    Verify, deduplicate, manipulate, and assign records automatically to keep your CRM data accurate, complete, and ready for business.
    Learn More
  • 1
    Amulet Map Editor

    Amulet Map Editor

    A new Minecraft world editor and converter

    The new age Minecraft world editor and converter that supports every version since Java 1.12 and Bedrock 1.7. Amulet is a Minecraft world editor built from the ground up with the lessons learnt from previous editors in mind. The program works natively with the block state format introduced in 1.13 which enables editing of all world formats. Amulet is built on top of a world converter that converts all world data into a custom superset format. This means that all worlds can be modified in the...
    Downloads: 663 This Week
    Last Update:
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  • 2
    DreamerV3

    DreamerV3

    Mastering Diverse Domains through World Models

    DreamerV3 is an open-source implementation of a reinforcement learning algorithm that uses world models to train intelligent agents capable of learning complex behaviors across many environments. The system works by building an internal model of the environment and then using that model to simulate possible future outcomes of actions, allowing the agent to learn from imagined experiences rather than only from real interactions. This approach enables the algorithm to efficiently learn...
    Downloads: 0 This Week
    Last Update:
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