2 projects for "model train design" with 2 filters applied:

  • Atera all-in-one platform IT management software with AI agents Icon
    Atera all-in-one platform IT management software with AI agents

    Ideal for internal IT departments or managed service providers (MSPs)

    Atera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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  • Desktop and Mobile Device Management Software Icon
    Desktop and Mobile Device Management Software

    It's a modern take on desktop management that can be scaled as per organizational needs.

    Desktop Central is a unified endpoint management (UEM) solution that helps in managing servers, laptops, desktops, smartphones, and tablets from a central location.
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  • 1

    DuranDuranbot

    Teachable/trainable artificially intelligent music bot

    A teachable/trainable artificially intelligent music bot fundamentally inspired by how the new wave band Duran Duran composes music. This program utilizes many algorithmic/AI techniques/processes, including machine learning; which allow you to teach/train it to compose music which you prefer... and the technique which is the foundation of the design of DuranDuranbot, which was directly inspired by how Duran Duran writes music........ Called, "bit by bit circular composition"....... and it's explanation can be found here - https://scsynth.org/t/bit-by-bit-circular-composition/1107 This program is written in the SuperCollider programming language - https://en.wikipedia.org/wiki/SuperCollider Contact - ken_brant@ymail.com
    Downloads: 1 This Week
    Last Update:
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  • 2
    Supervised Reptile

    Supervised Reptile

    Code for the paper "On First-Order Meta-Learning Algorithms"

    The supervised-reptile repository contains code associated with the paper “On First-Order Meta-Learning Algorithms”, which introduces Reptile, a meta-learning algorithm for learning model parameter initializations that adapt quickly to new tasks. The implementation here is aimed at supervised few-shot learning settings (e.g. Omniglot, Mini-ImageNet), not reinforcement learning, and includes scripts to run training and evaluation for few-shot classification. The fundamental idea is: sample a task, train on that task (inner loop), and then move the initialization parameters toward the adapted parameters (outer loop). ...
    Downloads: 0 This Week
    Last Update:
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