pytorch.org
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1. PyTorch
Link: https://pytorch.org/
Description: WebExplore a rich ecosystem of libraries, tools, and more to support development. Captum (“comprehension” in Latin) is an open source, extensible library for model interpretability built on PyTorch. PyTorch Geometric is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds.
DA: 28 PA: 98 MOZ Rank: 41
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2. Start Locally | PyTorch
Link: https://pytorch.org/get-started/locally/
Description: WebPreview (Nightly) Linux. Mac. Windows. Conda. Pip. LibTorch. Source. Python. C++ / Java. CUDA 11.8. CUDA 12.1. ROCm 5.7. CPU. pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118. Installing on Windows. PyTorch can be installed and used on various Windows distributions.
DA: 15 PA: 10 MOZ Rank: 19
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3. GitHub - pytorch/pytorch: Tensors and Dynamic neural networks …
Link: https://github.com/pytorch/pytorch
Description: WebPyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration. Deep neural networks built on a tape-based autograd system. You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
DA: 100 PA: 16 MOZ Rank: 69
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4. PyTorch - Wikipedia
Link: https://en.wikipedia.org/wiki/PyTorch
Description: WebPyTorch is a machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, originally developed by Meta AI and now part of the Linux Foundation umbrella.
DA: 80 PA: 100 MOZ Rank: 61
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5. PyTorch documentation — PyTorch 2.2 documentation
Link: https://pytorch.org/docs/stable/index.html
Description: WebPyTorch is an optimized tensor library for deep learning using GPUs and CPUs. Features described in this documentation are classified by release status: Stable: These features will be maintained long-term and there should generally be no major performance limitations or gaps in documentation.
DA: 96 PA: 38 MOZ Rank: 78
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6. Welcome to PyTorch Tutorials — PyTorch Tutorials 2.2.2+cu121 …
Link: https://pytorch.org/tutorials/
Description: WebLearn the Basics. Familiarize yourself with PyTorch concepts and modules. Learn how to load data, build deep neural networks, train and save your models in this quickstart guide. Get started with PyTorch.
DA: 8 PA: 1 MOZ Rank: 68
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7. PyTorch 2.0 | PyTorch
Link: https://pytorch.org/get-started/pytorch-2.0/
Description: WebWe are able to provide faster performance and support for Dynamic Shapes and Distributed. Below you will find all the information you need to better understand what PyTorch 2.0 is, where it’s going and more importantly how to get started today (e.g., tutorial, requirements, models, common FAQs).
DA: 2 PA: 76 MOZ Rank: 42
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8. Learn the Basics — PyTorch Tutorials 2.2.2+cu121 documentation
Link: https://pytorch.org/tutorials/beginner/basics/intro.html
Description: WebMost machine learning workflows involve working with data, creating models, optimizing model parameters, and saving the trained models. This tutorial introduces you to a complete ML workflow implemented in PyTorch, with links to learn more about each of …
DA: 21 PA: 30 MOZ Rank: 15
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9. Features | PyTorch
Link: https://pytorch.org/features/
Description: WebPyTorch enables fast, flexible experimentation and efficient production through a user-friendly front-end, distributed training, and ecosystem of tools and libraries. Get Started.
DA: 51 PA: 60 MOZ Rank: 95
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10. Learning PyTorch with Examples
Link: https://pytorch.org/tutorials/beginner/pytorch_with_examples.html
Description: WebYou can view our latest beginner content in Learn the Basics. This tutorial introduces the fundamental concepts of PyTorch through self-contained examples. At its core, PyTorch provides two main features: We will use a problem of fitting y=\sin (x) y = sin(x) with a third order polynomial as our running example.
DA: 3 PA: 8 MOZ Rank: 26
