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Source channel @githubtrending · Post #15611 · Apr 11

#other#awesome#awesome_list#design_systems#hacktoberfest#pattern_library#ui_library A design system is a collection of documentation, principles, and reusable elements that helps teams build digital products consistently. It includes UI components, pattern libraries, style guides, and guidelines for accessibility and user experience. The main benefit is that design systems enable teams to work faster and more efficiently by providing pre-built, standardized pieces they can reuse across projects, ensuring visual consistency and reducing redundancy while creating a shared language across your organization. https://github.com/alexpate/awesome-design-systems

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djangoproject

@djangoproject · Post #274 · 03/18/2017, 01:48 AM

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https://github.com/riga/tfdeploy Google's TensorFlow framework is taking off big-time now that it's at a full 1.0 release. One common question about it: How can I make use of the models I train in TensorFlow without using TensorFlow itself? #Tfdeploy is a partial answer to that question. It exports a trained TensorFlow model to "a simple #NumPy-based callable," meaning the model can be used in Python with Tfdeploy and the the NumPy math-and-stats library as the only dependencies. Most of the operations you can perform in TensorFlow can also be performed in Tfdeploy, and you can extend the behaviors of the library by way of standard Python metaphors (such as overloading a class). Now the bad news: Tfdeploy doesn't support GPU acceleration, if only because NumPy doesn't do that. Tfdeploy's creator suggests using the gNumPy project as a possible replacement. #Machine_learning

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