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  2. Torch (machine learning) - Wikipedia

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

    Many packages other than the above official packages are used with Torch. These are listed in the torch cheatsheet. [ 7 ] These extra packages provide a wide range of utilities such as parallelism, asynchronous input/output, image processing, and so on.

  3. 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. [ 24 ] 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 ...

  4. PyTorch Lightning - Wikipedia

    en.wikipedia.org/wiki/PyTorch_Lightning

    PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.

  5. Open Neural Network Exchange - Wikipedia

    en.wikipedia.org/wiki/Open_Neural_Network_Exchange

    The Open Neural Network Exchange (ONNX) [ˈɒnɪks] [2] is an open-source artificial intelligence ecosystem [3] of technology companies and research organizations that establish open standards for representing machine learning algorithms and software tools to promote innovation and collaboration in the AI sector.

  6. Google JAX - Wikipedia

    en.wikipedia.org/wiki/Google_JAX

    Google JAX is a machine learning framework for transforming numerical functions. [1] [2] [3] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and TensorFlow's XLA (Accelerated Linear Algebra).

  7. CatBoost - Wikipedia

    en.wikipedia.org/wiki/Catboost

    [11] along with TensorFlow, Pytorch, XGBoost and 8 other libraries. Kaggle listed CatBoost as one of the most frequently used machine learning (ML) frameworks in the world. It was listed as the top-8 most frequently used ML framework in the 2020 survey [12] and as the top-7 most frequently used ML framework in the 2021 survey. [13]

  8. Talk:PyTorch - Wikipedia

    en.wikipedia.org/wiki/Talk:PyTorch

    Is it necessary to have example code? This page contains some things which are more appropriate for the documentation. In general if people want to know this stuff they can read the Pytorch documentation, Wikipedia isn't really meant to be a hosting site for sample scripts — Preceding unsigned comment added by Gadget142 (talk • contribs) 00:16, 1 August 2023 (UTC) []

  9. PDF - Wikipedia

    en.wikipedia.org/wiki/PDF

    PDF's emphasis on preserving the visual appearance of documents across different software and hardware platforms poses challenges to the conversion of PDF documents to other file formats and the targeted extraction of information, such as text, images, tables, bibliographic information, and document metadata. Numerous tools and source code ...