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  2. Python (programming language) - Wikipedia

    en.wikipedia.org/wiki/Python_(programming_language)

    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. [36] Python consistently ranks as one of the most popular programming languages, and has gained widespread use in the machine learning community. [37] [38] [39] [40]

  3. PyPy - Wikipedia

    en.wikipedia.org/wiki/PyPy

    PyPy. PyPy ( / ˈpaɪpaɪ /) is an implementation of the Python programming language. [ 2] PyPy often runs faster than the standard implementation CPython because PyPy uses a just-in-time compiler. [ 3] Most Python code runs well on PyPy except for code that depends on CPython extensions, which either does not work or incurs some overhead when ...

  4. Numba - Wikipedia

    en.wikipedia.org/wiki/Numba

    Numba. Numba is an open-source JIT compiler that translates a subset of Python and NumPy into fast machine code using LLVM, via the llvmlite Python package. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes. Numba was started by Travis Oliphant in 2012 and has since been under ...

  5. CuPy - Wikipedia

    en.wikipedia.org/wiki/CuPy

    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.

  6. Anaconda (Python distribution) - Wikipedia

    en.wikipedia.org/wiki/Anaconda_(Python_distribution)

    Anaconda is a distribution of the Python and R programming languages for scientific computing ( data science, machine learning applications, large-scale data processing, predictive analytics, etc.), that aims to simplify package management and deployment. The distribution includes data-science packages suitable for Windows, Linux, and macOS.

  7. Comparison of linear algebra libraries - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_linear...

    uBLAS is a C++ template class library that provides BLAS level 1, 2, 3 functionality for dense, packed and sparse matrices. Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms. Fastor is a high performance tensor (fixed multi-dimensional array) library for modern C++.

  8. History of Python - Wikipedia

    en.wikipedia.org/wiki/History_of_Python

    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. [ 27 ] [ 10 ] Similarly, Python 2.7 coincided with and included features from Python 3.1, [ 28 ] which was released on June 26, 2009.

  9. IronPython - Wikipedia

    en.wikipedia.org/wiki/IronPython

    It is free and open-source software, and can be implemented with Python Tools for Visual Studio, which is a free and open-source extension for Microsoft's Visual Studio IDE. [ 2][ 3] IronPython is written entirely in C#, although some of its code is automatically generated by a code generator written in Python.