Showing 5 open source projects for "no code ai design"

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  • Outgrown Windows Task Scheduler? Icon
    Outgrown Windows Task Scheduler?

    Free diagnostic identifies where your workflow is breaking down—with instant analysis of your scheduling environment.

    Windows Task Scheduler wasn't built for complex, cross-platform automation. Get a free diagnostic that shows exactly where things are failing and provides remediation recommendations. Interactive HTML report delivered in minutes.
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  • Vibes don’t ship, Retool does Icon
    Vibes don’t ship, Retool does

    Start from a prompt and build production-ready apps on your data—with security, permissions, and compliance built in.

    Vibe coding tools create cool demos, but Retool helps you build software your company can actually use. Generate internal apps that connect directly to your data—deployed in your cloud with enterprise security from day one. Build dashboards, admin panels, and workflows with granular permissions already in place. Stop prototyping and ship on a platform that actually passes security review.
    Build apps that ship
  • 1
    DeepDetect

    DeepDetect

    Deep Learning API and Server in C++14 support for Caffe, PyTorch

    The core idea is to remove the error sources and difficulties of Deep Learning applications by providing a safe haven of commoditized practices, all available as a single core. While the Open Source Deep Learning Server is the core element, with REST API, and multi-platform support that allows training & inference everywhere, the Deep Learning Platform allows higher level management for training neural network models and using them as if they were simple code snippets. Ready for applications...
    Downloads: 0 This Week
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  • 2
    Hello AI World

    Hello AI World

    Guide to deploying deep-learning inference networks

    ...Follow these directions to integrate deep learning into your platform of choice and quickly develop a proof-of-concept design.
    Downloads: 0 This Week
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  • 3
    PyTorch Transfer-Learning-Library

    PyTorch Transfer-Learning-Library

    Transfer Learning Library for Domain Adaptation, Task Adaptation, etc.

    TLlib is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms or readily apply existing algorithms. We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.
    Downloads: 0 This Week
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  • 4
    Detectron2

    Detectron2

    Next-generation platform for object detection and segmentation

    Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc.
    Downloads: 0 This Week
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  • 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.
    Learn More
  • 5
    TNN

    TNN

    Uniform deep learning inference framework for mobile

    ...On the mobile phone, TNN has been used by many applications such as mobile QQ, weishi, and Pitu. As a basic acceleration framework for Tencent Cloud AI, TNN has provided acceleration support for the implementation of many businesses. Everyone is welcome to participate in the collaborative construction to promote the further improvement of the TNN inference framework.
    Downloads: 0 This Week
    Last Update:
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