Open Source Software

  1. Microsoft is perhaps the most impressive company on the planet right now. While it doesn’t (currently) dominate markets like it used to, Microsoft has managed something dramatically more difficult, something that portends future success as a platform behemoth: profound cultural change.

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    (Insider Story)
  2. Microsoft has released the 0.6 version of its ML.Net machine learning framework, aimed at .Net developers. The update adds a new and more useful model-building API set, the ability to use more existing models to provide predictions, and better performance overall.

    The original ML.Net API limited the kinds of pipelines you could build and had some clumsy restrictions on labeling and scoring data. The new API more flexibly allows training and prediction processes to be made up of multiple components that can be joined together in a variety of combinations, instead of requiring a single linear pipeline. The goal is to emulate the design of APIs used to drive other frameworks like Apache Spark.

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  3. Microsoft’s open source development tool is an important piece of the developer’s toolkit. Built using GitHub’s cross-platform Electron framework, Visual Studio Code is a full-featured development editor that supports a wide selection of languages and platforms, from the familiar C and C# to modern environments and languages like Go and Node.js, with parity between Windows, MacOS, and Linux releases.

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    (Insider Story)
  4. The JS Foundation is the home of prominent open source JavaScript projects, most notably the popular jQuery JavaScript library. But it also has lower-profile efforts that developers might benefit from, for a variety of uses such as cloud provisioning, the internet of things (IoT), payments, and Node.js programming.

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    (Insider Story)
  5. An official release candidate of PyTorch 1.0, the Python-centric deep learning framework created by Facebook, is available for developer testing. One of the most touted features of the new release is the ability to define models by writing Python code that can be selectively accelerated—similar to how competing frameworks work.

    Python’s traditional role in machine learning has been to wrap high-speed, back-end code libraries with easy-to-use, front-end syntax. Anyone who writes machine learning modules in Python quickly discovers that native Python isn’t nearly fast enough for performance-critical research work or production use.

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