build model formatting fixes

beginner_source/quickstart/build_model_tutorial.py

@@ -1,8 +1,28 @@
 """
-Build Model Tutorial
-============================
+Build the Neural Netowrk
+===================
 """
 
+#################################################################
+# Get Started Building the Model
+# -----------------
+#
+# The data has been loaded and transformed we can now build the model. 
+# We will leverage `torch.nn <https://pytorch.org/docs/stable/nn.html>`_ predefined layers that PyTorch has that can simplify our code.
+# 
+# In the below example, for our FashionMNIT image dataset, we are using a `Sequential` 
+# container from class `torch.nn. Sequential <https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html>`_ 
+# that allows us to define the model layers inline. In the "Sequential" in-line model building format the ``forward()`` 
+# method is created for you and the modules you add are passed in as a list or dictionary in the order that are they are defined.
+# 
+# Another way to bulid this model is with a class 
+# using `nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html)>`_ 
+# A big plus with using a class that inherits ``nn.Module`` is better parameter management across all nested submodules.
+# This gives us more flexibility, because we can construct layers of any complexity, including the ones with shared weights. 
+# 
+# Lets break down the steps to build this model below
+# 
+
 ##########################################
 # The data has been loaded and transformed we can now build the model. 
 # We will leverage `torch.nn <https://pytorch.org/docs/stable/nn.html>`_ predefined layers that PyTorch has that can simplify our code.
@@ -109,6 +129,7 @@ def forward(self, x):
 #
 # From the docs:
 # ``torch.nn.Flatten(start_dim: int = 1, end_dim: int = -1)``
+#
 # Here is an example using one of the training_data set items:
 tensor = training_data[0][0]
 print(tensor.size())
@@ -168,7 +189,7 @@ def forward(self, x):
 # --------------------------------
 #
 # In the class implementation of the neural network we define a ``forward`` function.  
-# Then call the ``NeuralNetwork``class and assign the device. When training the model we will call ``model``
+# Then call the ``NeuralNetwork`` class and assign the device. When training the model we will call ``model``
 # and pass the data (x) into the forward function and through each layer of our network.
 #
 #