Showing 1 open source project for "mediatek universal tool"

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  • ACI Learning: Internal Audit, Cybersecurity, and IT Training Icon
    ACI Learning: Internal Audit, Cybersecurity, and IT Training

    Proven skill building for every aspect of your support or IT team.

    Traditional training doesn't equip employees with the practical skills they need to drive business success. ACI Learning provides hands-on IT and cybersecurity training designed to build real-world, on-the-job skills. Our outcome-based programs empower employees with certification prep, industry-recognized credentials, and flexible learning options. With expert-led video training, labs, and scalable solutions, we help businesses, individuals, governments, and academic institutions develop a skilled workforce, align with business goals, and stay ahead in a rapidly evolving digital world.
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  • Stigg | SaaS Monetization and Entitlements API Icon
    Stigg | SaaS Monetization and Entitlements API

    For developers in need of a tool to launch pricing plans faster and build better buying experiences

    A monetization platform is a standalone middleware that sits between your application and your business applications, as part of the modern enterprise billing stack. Stigg unifies all the APIs and abstractions billing and platform engineers had to build and maintain in-house otherwise. Acting as your centralized source of truth, with a highly scalable and flexible entitlements management, rolling out any pricing and packaging change is now a self-service, risk-free, exercise.
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    MMdnn

    MMdnn

    Tools to help users inter-operate among 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
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