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Project Jupyter's name is a reference to the three core programming languages supported by Jupyter, which are Julia, Python and R. Its name and logo are an homage to Galileo's discovery of the moons of Jupiter, as documented in notebooks attributed to Galileo. Jupyter is financially sponsored by NumFOCUS. [1]
Kolab is a free and open source groupware suite. It consists of the Kolab server and a wide variety of Kolab clients, including KDE PIM-Suite Kontact, Roundcube web frontend, Mozilla Thunderbird and Mozilla Lightning with SyncKolab extension and Microsoft Outlook with proprietary Kolab-Connector PlugIns.
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 ]
Mojo was created for an easy transition from Python. The language has syntax similar to Python's, with inferred static typing, [30] and allows users to import Python modules. [31] It uses LLVM and MLIR as its compilation backend. [12] [32] [33] The language also intends to add a foreign function interface to call C/C++ and Python
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]
Imagine stumbling out of bed and accidentally stepping on a snake. It’s the stuff of nightmares! Yet, it happens, especially in the warmer months. Snakes tend to make themselves known during ...
CIUDAD JUÁREZ, Mexico – When Mexico's president offered to send 10,000 troops to the U.S. border, it was an easy response to President Donald Trump's tariff threats. Claudia Sheinbaum Pardo has ...
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.