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Sep 29, 2025
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Learning PyTorch with Examples Audit #3600

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ca19641
remove empty example section
jiannanWang Sep 29, 2025
3930463
Merge branch 'main' into jiannanWang/examples
jiannanWang Sep 29, 2025
46e6463
Add prerequisite.
jiannanWang Sep 29, 2025
5409cd3
Revert pytorch_with_examples and remove :hidden:
jiannanWang Sep 29, 2025
4dba7db
Merge branch 'main' into jiannanWang/examples
jiannanWang Sep 29, 2025
ade91f7
fix `pi` to `\pi`
jiannanWang Sep 29, 2025
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2 changes: 1 addition & 1 deletion beginner_source/examples_nn/polynomial_nn.py
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Expand Up @@ -4,7 +4,7 @@
-----------
A third order polynomial, trained to predict :math:`y=\sin(x)` from :math:`-\pi`
to :math:`pi` by minimizing squared Euclidean distance.
to :math:`\pi` by minimizing squared Euclidean distance.
This implementation uses the nn package from PyTorch to build the network.
PyTorch autograd makes it easy to define computational graphs and take gradients,
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2 changes: 1 addition & 1 deletion beginner_source/examples_nn/polynomial_optim.py
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--------------

A third order polynomial, trained to predict :math:`y=\sin(x)` from :math:`-\pi`
to :math:`pi` by minimizing squared Euclidean distance.
to :math:`\pi` by minimizing squared Euclidean distance.

This implementation uses the nn package from PyTorch to build the network.

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2 changes: 1 addition & 1 deletion beginner_source/examples_tensor/polynomial_numpy.py
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Expand Up @@ -4,7 +4,7 @@
--------------
A third order polynomial, trained to predict :math:`y=\sin(x)` from :math:`-\pi`
to :math:`pi` by minimizing squared Euclidean distance.
to :math:`\pi` by minimizing squared Euclidean distance.
This implementation uses numpy to manually compute the forward pass, loss, and
backward pass.
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2 changes: 1 addition & 1 deletion beginner_source/examples_tensor/polynomial_tensor.py
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Expand Up @@ -4,7 +4,7 @@
----------------

A third order polynomial, trained to predict :math:`y=\sin(x)` from :math:`-\pi`
to :math:`pi` by minimizing squared Euclidean distance.
to :math:`\pi` by minimizing squared Euclidean distance.

This implementation uses PyTorch tensors to manually compute the forward pass,
loss, and backward pass.
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13 changes: 9 additions & 4 deletions beginner_source/pytorch_with_examples.rst
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Expand Up @@ -26,6 +26,12 @@ between the network output and the true output.
You can browse the individual examples at the
:ref:`end of this page <examples-download>`.

To run the tutorials below, make sure you have the `torch`_
and `numpy`_ packages installed.

.. _torch: https://github.com/pytorch/pytorch
.. _numpy: https://github.com/numpy/numpy

.. contents:: Table of Contents
:local:

Expand Down Expand Up @@ -217,6 +223,8 @@ We can easily implement this model as a Module subclass:
.. includenodoc:: /beginner/examples_nn/dynamic_net.py




.. _examples-download:

Examples
Expand All @@ -229,7 +237,6 @@ Tensors

.. toctree::
:maxdepth: 2
:hidden:

/beginner/examples_tensor/polynomial_numpy
/beginner/examples_tensor/polynomial_tensor
Expand All @@ -247,7 +254,6 @@ Autograd

.. toctree::
:maxdepth: 2
:hidden:

/beginner/examples_autograd/polynomial_autograd
/beginner/examples_autograd/polynomial_custom_function
Expand All @@ -266,7 +272,6 @@ Autograd

.. toctree::
:maxdepth: 2
:hidden:

/beginner/examples_nn/polynomial_nn
/beginner/examples_nn/polynomial_optim
Expand All @@ -284,4 +289,4 @@ Autograd

.. raw:: html

<div style='clear:both'></div>
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