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  2. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [23] PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo, a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and ...

  3. Conda (package manager) - Wikipedia

    en.wikipedia.org/wiki/Conda_(Package_Manager)

    Conda is an open-source, [2] cross-platform, [3] language-agnostic package manager and environment management system. It was originally developed to solve package management challenges faced by Python data scientists , and today is a popular package manager for Python and R .

  4. CUDA - Wikipedia

    en.wikipedia.org/wiki/CUDA

    In computing, CUDA (Compute Unified Device Architecture) is a proprietary [2] parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs.

  5. Anaconda (installer) - Wikipedia

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

    Anaconda is a free and open-source system installer for Linux distributions.. Anaconda is used by Red Hat Enterprise Linux, Oracle Linux, Scientific Linux, Rocky Linux, AlmaLinux, CentOS, MIRACLE LINUX, Qubes OS, Fedora, Sabayon Linux and BLAG Linux and GNU, also in some less known and discontinued distros like Progeny Componentized Linux, Asianux, Foresight Linux, Rpath Linux and VidaLinux.

  6. Torch (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Torch_(machine_learning)

    The torch package also simplifies object-oriented programming and serialization by providing various convenience functions which are used throughout its packages. The torch.class(classname, parentclass) function can be used to create object factories ().

  7. 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]

  8. CatBoost - Wikipedia

    en.wikipedia.org/wiki/Catboost

    It works on Linux, Windows, macOS, and is available in Python, [8] R, [9] and models built using CatBoost can be used for predictions in C++, Java, [10] C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. [6] InfoWorld magazine awarded the library "The best machine learning tools" in 2017.

  9. Gensim - Wikipedia

    en.wikipedia.org/wiki/Gensim

    Gensim includes streamed parallelized implementations of fastText, [2] word2vec and doc2vec algorithms, [3] as well as latent semantic analysis (LSA, LSI, SVD), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), tf-idf and random projections.