Showing 2 open source projects for "acme cross-assembler code"

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    Skillfully - The future of skills based hiring

    Realistic Workplace Simulations that Show Applicant Skills in Action

    Skillfully transforms hiring through AI-powered skill simulations that show you how candidates actually perform before you hire them. Our platform helps companies cut through AI-generated resumes and rehearsed interviews by validating real capabilities in action. Through dynamic job specific simulations and skill-based assessments, companies like Bloomberg and McKinsey have cut screening time by 50% while dramatically improving hire quality.
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  • Cloudbrink Personal SASE service Icon
    Cloudbrink Personal SASE service

    For companies looking for low maintenance, secure, high performance connectivity for hybrid and remote workers

    Cloudbrink’s Personal SASE is a high-performance connectivity and security service that delivers a lightning-fast, in-office experience to the modern hybrid workforce anywhere. Combining high-performance ZTNA with Automated Moving Target Defense (AMTD), and Personal SD-WAN all connections are ultra-secure.
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    Avogadro

    Avogadro

    An intuitive molecular editor and visualization tool

    Avogadro is an advanced molecular editor designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science and related areas. It offers a flexible rendering framework and a powerful plugin architecture.
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    Downloads: 1,145 This Week
    Last Update:
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  • 2
    MMdnn

    MMdnn

    Tools to help users inter-operate among deep learning frameworks

    MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. We implement a universal converter to convert DL models between frameworks, which means you can train a model with one framework and deploy it with another. During the model conversion, we generate some code snippets to simplify later retraining or inference. ...
    Downloads: 0 This Week
    Last Update:
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