Learning PyTorch with Examples Audit (#3600)

Fixes
[T237454989](https://www.internalfb.com/intern/tasks/?t=237454989).

Ran and verified that these examples work:

beginner_source/examples_tensor/polynomial_numpy.py
beginner_source/examples_tensor/polynomial_tensor.py
beginner_source/examples_autograd/polynomial_autograd.py
beginner_source/examples_autograd/polynomial_custom_function.py
beginner_source/examples_nn/dynamic_net.py
beginner_source/examples_nn/polynomial_module.py
beginner_source/examples_nn/polynomial_module.py
beginner_source/examples_nn/polynomial_optim.py

Author: jiannanWang

beginner_source/examples_nn/polynomial_nn.py

@@ -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,

beginner_source/examples_nn/polynomial_optim.py

@@ -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.
 

beginner_source/examples_tensor/polynomial_numpy.py

@@ -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.

beginner_source/examples_tensor/polynomial_tensor.py

@@ -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.

beginner_source/pytorch_with_examples.rst

@@ -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:
 
@@ -217,6 +223,8 @@ We can easily implement this model as a Module subclass:
 .. includenodoc:: /beginner/examples_nn/dynamic_net.py
 
 
+
+
 .. _examples-download:
 
 Examples
@@ -229,7 +237,6 @@ Tensors
 
 .. toctree::
    :maxdepth: 2
-   :hidden:
 
    /beginner/examples_tensor/polynomial_numpy
    /beginner/examples_tensor/polynomial_tensor
@@ -247,7 +254,6 @@ Autograd
 
 .. toctree::
    :maxdepth: 2
-   :hidden:
 
    /beginner/examples_autograd/polynomial_autograd
    /beginner/examples_autograd/polynomial_custom_function
@@ -266,7 +272,6 @@ Autograd
 
 .. toctree::
    :maxdepth: 2
-   :hidden:
 
    /beginner/examples_nn/polynomial_nn
    /beginner/examples_nn/polynomial_optim
@@ -284,4 +289,4 @@ Autograd
 
 .. raw:: html
 
-    <div style='clear:both'></div>
+    <div style='clear:both'></div>