Showing 4 open source projects for "net speed tools"

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    Automate contact and company data extraction

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  • 1
    Tangent

    Tangent

    Source-to-source debuggable derivatives in pure Python

    ...In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools. As a result, you can finally read your automatic derivative code just like the rest of your program. Tangent is useful to researchers and students who not only want to write their models in Python, but also read and debug automatically-generated derivative code without sacrificing speed and flexibility. Tangent works on a large and growing subset of Python, provides extra autodiff features other Python ML libraries don't have, has reasonable performance, and is compatible with TensorFlow and NumPy.
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  • 2
    The Language Processing Agency provides agents and an agent-oriented infrastructure for developing language processors in .NET. Typical language processors are transformation tools, extraction tools, language interpreters, etc.
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  • 3
    An implementation of Kent Beck's famous XUnit testing framework designed specifically for unit testing components written for the .NET platform.
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  • 4
    This project aims at being the spearhead of logging tools. It will integrate with existing logging frameworks and will incorporate unique features to maximize speed and usability.
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