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        "[Introduction](introyt1_tutorial.html) \\|\\|\n[Tensors](tensors_deeper_tutorial.html) \\|\\|\n[Autograd](autogradyt_tutorial.html) \\|\\| [Building\nModels](modelsyt_tutorial.html) \\|\\| [TensorBoard\nSupport](tensorboardyt_tutorial.html) \\|\\| [Training\nModels](trainingyt.html) \\|\\| [Model Understanding](captumyt.html)\n\nIntroduction to PyTorch - YouTube Series\n========================================\n\nAuthors: [Brad Heintz](https://github.com/fbbradheintz)\n\nThis tutorial follows along with the [PyTorch Beginner\nSeries](https://www.youtube.com/playlist?list=PL_lsbAsL_o2CTlGHgMxNrKhzP97BaG9ZN)\non YouTube.\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-   **On the cloud**: This is the easiest way to get started! Each\n    section has a Colab link at the top, which opens a notebook with the\n    code in a fully-hosted environment. Pro tip: Use Colab with a GPU\n    runtime to speed up operations *Runtime \\> Change runtime type \\>\n    GPU*\n-   **Locally**: This option requires you to set up PyTorch and\n    torchvision 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\n::: {.toctree maxdepth=\"2\" hidden=\"\"}\nintroyt1\\_tutorial tensors\\_deeper\\_tutorial autogradyt\\_tutorial\nmodelsyt\\_tutorial tensorboardyt\\_tutorial trainingyt captumyt\n:::\n"
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