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        "**Learn the Basics** \\|\\| [Quickstart](quickstart_tutorial.html) \\|\\|\n[Tensors](tensorqs_tutorial.html) \\|\\| [Datasets &\nDataLoaders](data_tutorial.html) \\|\\|\n[Transforms](transforms_tutorial.html) \\|\\| [Build\nModel](buildmodel_tutorial.html) \\|\\|\n[Autograd](autogradqs_tutorial.html) \\|\\|\n[Optimization](optimization_tutorial.html) \\|\\| [Save & Load\nModel](saveloadrun_tutorial.html)\n\nLearn the Basics\n================\n\nAuthors: [Suraj Subramanian](https://github.com/subramen), [Seth\nJuarez](https://github.com/sethjuarez/), [Cassie\nBreviu](https://github.com/cassiebreviu/), [Dmitry\nSoshnikov](https://soshnikov.com/), [Ari\nBornstein](https://github.com/aribornstein/)\n\nMost machine learning workflows involve working with data, creating\nmodels, optimizing model parameters, and saving the trained models. This\ntutorial introduces you to a complete ML workflow implemented in\nPyTorch, with links to learn more about each of these concepts.\n\nWe\\'ll use the FashionMNIST dataset to train a neural network that\npredicts if an input image belongs to one of the following classes:\nT-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker,\nBag, or Ankle boot.\n\n[This tutorial assumes a basic familiarity with Python and Deep Learning\nconcepts.]{.title-ref}\n\nRunning the Tutorial Code\n-------------------------\n\nYou can run this tutorial in a couple of ways:\n\n-   **In the cloud**: This is the easiest way to get started! Each\n    section has a \\\"Run in Microsoft Learn\\\" and \\\"Run in Google Colab\\\"\n    link at the top, which opens an integrated notebook in Microsoft\n    Learn or Google Colab, respectively, with the code in a fully-hosted\n    environment.\n-   **Locally**: This option requires you to setup PyTorch and\n    TorchVision first on your local machine ([installation\n    instructions](https://pytorch.org/get-started/locally/)). Download\n    the notebook or copy the code into your favorite IDE.\n\nHow to Use this Guide\n---------------------\n\nIf you\\'re familiar with other deep learning frameworks, check out the\n[0. Quickstart](quickstart_tutorial.html) first to quickly familiarize\nyourself with PyTorch\\'s API.\n\nIf you\\'re new to deep learning frameworks, head right into the first\nsection of our step-by-step guide: [1. Tensors](tensor_tutorial.html).\n\n::: {.toctree maxdepth=\"2\" hidden=\"\"}\nquickstart\\_tutorial tensorqs\\_tutorial data\\_tutorial\ntransforms\\_tutorial buildmodel\\_tutorial autogradqs\\_tutorial\noptimization\\_tutorial saveloadrun\\_tutorial\n:::\n"
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