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Source channel @githubtrending · Post #14840 · Jun 19

#other This collection of Alfred workflows lets you automate many tasks on your Mac, like showing charger power, generating random images, fixing zoom issues, editing SVGs, converting CSS styles, managing Git projects, switching audio devices, and more. These workflows save you time by performing complex or repetitive actions with simple commands or shortcuts. For example, you can quickly clone Git repos, switch audio inputs, or convert timestamps without leaving your keyboard. Using these tools boosts your productivity by making your computer easier and faster to control, especially if you use Alfred’s Powerpack to integrate and customize workflows for your needs[1][2][5]. https://github.com/sunzsh/favoritesWorkflow4Alfred

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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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