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Source channel @githubtrending · Post #15037 · Aug 8

#other The FFmpeg School of Assembly Language teaches you how assembly code is written in FFmpeg, helping you understand what happens inside your computer. To join, you should know C programming (especially pointers) and basic high school math. The lessons include assignments and a Discord server for questions. By completing them, you can contribute to FFmpeg, a powerful video processing tool that uses assembly to speed up tasks dramatically—sometimes up to 94 times faster with special instructions like AVX-512. Learning this helps you write highly efficient code for video and multimedia processing, improving performance in real-world applications. https://github.com/FFmpeg/asm-lessons

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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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