Apply suggestions from code review

Co-authored-by: Ethan Harris <[email protected]>
Co-authored-by: thomas chaton <[email protected]>

Author: 3 people

docs/source/starter/introduction_guide.rst

@@ -99,7 +99,7 @@ Let's first start with the model. In this case, we'll design a 3-layer neural ne
             return x
 
 Notice this is a :doc:`lightning module <../common/lightning_module>` instead of a ``torch.nn.Module``. A LightningModule is
-equivalent to a pure PyTorch module except it has added functionality. However, you can use it **exactly** the same as you would a PyTorch module.
+equivalent to a pure PyTorch ``nn.Module`` except it has added functionality. However, you can use it **exactly** the same as you would a PyTorch ``nn.Module``.
 
 .. testcode::
 

docs/source/starter/lightning_lite.rst

@@ -221,7 +221,7 @@ but there are several major challenges ahead of you now:
    :header-rows: 0
 
    * - Processes divergence
-     - This happens when processes execute a different section of the code due to different if/else conditions, race conditions on existing files, etc., resulting in hanging.
+     - This happens when processes execute a different section of the code due to different if/else conditions, race conditions on existing files and so on, resulting in hanging.
    * - Cross processes reduction
      - Miscalculated metrics or gradients due to errors in their reduction.
    * - Large sharded models
@@ -416,7 +416,7 @@ Configure the devices to run on. Can be of type:
     # equivalent
     lite = Lite(devices=0)
 
-    # int: run on to GPUs
+    # int: run on two GPUs
     lite = Lite(devices=2, accelerator="gpu")
 
     # list: run on GPUs 1, 4 (by bus ordering)
@@ -695,7 +695,7 @@ load
 ====
 
 Load checkpoint contents from a file. Replaces all occurrences of ``torch.load(...)`` in your code. Lite will take care of
-handling the loading part correctly, no matter if you are running a single device, multi-devices or multi-nodes.
+handling the loading part correctly, no matter if you are running a single device, multi-device, or multi-node.
 
 .. code-block:: python
 

docs/source/starter/new-project.rst

@@ -380,9 +380,9 @@ You can also add a forward method to do predictions however you want.
 
 .. code-block:: python
 
-    # ----------------------------------
+    # -------------------------------
     # using the AE to generate images
-    # ----------------------------------
+    # -------------------------------
     class LitAutoEncoder(LightningModule):
         def __init__(self):
             super().__init__()