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Revert pytorch_with_examples and remove :hidden:
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beginner_source/pytorch_with_examples.rst

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@@ -22,6 +22,10 @@ as our running example. The network will have four parameters, and will be train
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gradient descent to fit random data by minimizing the Euclidean distance
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between the network output and the true output.
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.. note::
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You can browse the individual examples at the
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:ref:`end of this page <examples-download>`.
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To run the tutorials below, make sure you have the `torch`_
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and `numpy`_ packages installed.
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@@ -217,3 +221,72 @@ times when defining the forward pass.
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We can easily implement this model as a Module subclass:
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.. includenodoc:: /beginner/examples_nn/dynamic_net.py
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.. _examples-download:
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Examples
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~~~~~~~~
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You can browse the above examples here.
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Tensors
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-------
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.. toctree::
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:maxdepth: 2
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/beginner/examples_tensor/polynomial_numpy
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/beginner/examples_tensor/polynomial_tensor
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.. galleryitem:: /beginner/examples_tensor/polynomial_numpy.py
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.. galleryitem:: /beginner/examples_tensor/polynomial_tensor.py
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.. raw:: html
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<div style='clear:both'></div>
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Autograd
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--------
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.. toctree::
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:maxdepth: 2
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/beginner/examples_autograd/polynomial_autograd
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/beginner/examples_autograd/polynomial_custom_function
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.. galleryitem:: /beginner/examples_autograd/polynomial_autograd.py
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.. galleryitem:: /beginner/examples_autograd/polynomial_custom_function.py
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.. raw:: html
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<div style='clear:both'></div>
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``nn`` module
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--------------
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.. toctree::
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:maxdepth: 2
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/beginner/examples_nn/polynomial_nn
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/beginner/examples_nn/polynomial_optim
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/beginner/examples_nn/polynomial_module
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/beginner/examples_nn/dynamic_net
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.. galleryitem:: /beginner/examples_nn/polynomial_nn.py
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.. galleryitem:: /beginner/examples_nn/polynomial_optim.py
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.. galleryitem:: /beginner/examples_nn/polynomial_module.py
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.. galleryitem:: /beginner/examples_nn/dynamic_net.py
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.. raw:: html
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<div style='clear:both'></div>

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