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Source channel @githubtrending · Post #14888 · Jun 30

#typescript#data_visualization#geospatial_analysis#javascript#maps#python#visualization#webgl deck.gl is a powerful tool that helps you create fast, interactive, and visually impressive maps and data visualizations using WebGL technology. It lets you turn large sets of data into layers like icons, polygons, and text, which you can view in different ways such as maps or 3D scenes. It works well with popular map providers like Google Maps and Mapbox, and supports easy interaction like clicking and filtering. You can use it simply by adding a script or installing it via npm or Python. This makes it easier for you to build custom, high-performance visualizations quickly and with less coding effort. https://github.com/visgl/deck.gl

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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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