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Source channel @githubtrending · Post #14843 · Jun 19

#c_lang ESP-IDF is Espressif's official software framework for developing applications on ESP32 and related chips, supporting Windows, Linux, and macOS. It offers a complete set of tools, libraries, and drivers for Wi-Fi, Bluetooth, and IoT features, enabling you to build connected devices efficiently using C or C++. ESP-IDF supports multiple chip versions with stable releases and ongoing updates, ensuring reliability and production readiness. It includes easy commands for building, flashing, and monitoring your projects, plus example templates to start quickly. Using ESP-IDF helps you create robust, feature-rich IoT applications with strong community and official support. This saves time and effort in development and deployment. https://github.com/espressif/esp-idf

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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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