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Source channel @githubtrending · Post #14966 · Jul 16

#other#awesome#awesome_list#c#c_plus_plus#cpp#cpp_library#cppcon#libraries#list#lists#programming_tutorial#resources You can access a vast, well-organized collection of C++ libraries, frameworks, and tools that cover almost every programming need—from standard libraries, GUI, networking, and machine learning to game engines, cryptography, and more. This curated list includes popular and high-quality options like Boost, Qt, OpenCV, and many specialized libraries for tasks such as asynchronous programming, audio processing, and serialization. Using these resources can save you time, improve code quality, and help you build efficient, robust applications by leveraging tested, peer-reviewed components instead of writing everything from scratch. It’s a one-stop reference to boost your C++ development productivity and capabilities. https://github.com/fffaraz/awesome-cpp

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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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