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Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2. [37] Python consistently ranks as one of the most popular programming languages, and has gained widespread use in the machine learning community. [38] [39] [40] [41]
Python 2.6 was released to coincide with Python 3.0, and included some features from that release, as well as a "warnings" mode that highlighted the use of features that were removed in Python 3.0. [ 28 ] [ 10 ] Similarly, Python 2.7 coincided with and included features from Python 3.1, [ 29 ] which was released on June 26, 2009.
Python Imaging Library is a free and open-source additional library for the Python programming language that adds support for opening, manipulating, and saving many different image file formats. It is available for Windows, Mac OS X and Linux. The latest version of PIL is 1.1.7, was released in September 2009 and supports Python 1.5.2–2.7. [3]
casks on https://formulae.brew.sh, search on Homebrew sites and better Docker support. 2.0.0 2019-02-02 Mavericks (10.9) official support for Linux and Windows 10 (with Windows Subsystem for Linux), brew cleanup running automatically, no more options in Homebrew/homebrew-core, and removal of support for OS X Mountain Lion (10.8) and older. 1.9.0
Flexible access to WebSphere Liberty is provided through additional distributions as a docker image [22] and Cloud Foundry buildpack. [23] In September 2017 IBM moved ongoing development of Liberty into a new Open Source project called Open Liberty. [24] Open Liberty is the source for the Liberty runtime in WebSphere Application Server.
Visual Studio 2017 offers new features like support for EditorConfig (a coding style enforcement framework), NGen support, .NET Core and Docker toolset (Preview), and Xamarin 4.3 (Preview). [211] It also has a XAML Editor, improved IntelliSense, live unit testing, debugging enhancement and better IDE experience and productivity.
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
Numpy is one of the most popular Python data libraries, and TensorFlow offers integration and compatibility with its data structures. [66] Numpy NDarrays, the library's native datatype, are automatically converted to TensorFlow Tensors in TF operations; the same is also true vice versa. [ 66 ]