message: minor fixes to new "low_level_intro"
diff: @@ -63,8 +63,8 @@ TensorFlow uses numpy arrays to represent tensor **values**.
  You might think of TensorFlow Core programs as consisting of two discrete
  sections:
  -1. Building the computational graph (a `@{tf.Graph}`).
  -2. Running the computational graph (using a `@{tf.Session}`).
  +1. Building the computational graph (a @{tf.Graph}).
  +2. Running the computational graph (using a @{tf.Session}).
  ### Graph
  @@ -78,7 +78,7 @@ graph. The graph is composed of two types of objects.
  `tf.Tensors`.
  Important: `tf.Tensors` do not have values, they are just handles to elements
  -in computation graph.
  +in the computation graph.
  Let's build a simple computational graph. The most basic operation is a
  constant. The Python function that builds the operation takes a tensor value as
  @@ -180,7 +180,9 @@ print(sess.run({'ab':(a, b), 'total':total}))
  which returns the results in a structure of the same layout:
  +``` None
  {'total': 7.0, 'ab': (3.0, 4.0)}
  +```
  During a call to `tf.Session.run` any `tf.Tensor` only has a single value.
  For example, the following code calls `tf.random_uniform` to produce a
  @@ -252,10 +254,9 @@ that placeholders throw an error if no value is fed to them.
  Placeholders work for simple experiments, but @{tf.data$Datasets} are the
  preferred method of streaming data into a model.
  -Datasets also build up a representation of a calculation step by step, but do
  -not build Operations and Tensors directly into the Graph. To get a runnable
  -Tensor from a Dataset you must first convert it to a @{tf.data.Iterator}, and
  -then call the Iterator's @{tf.data.Iterator.get_next$`get_next`} method.
  +To get a runnable `tf.Tensor` from a Dataset you must first convert it to a
  +@{tf.data.Iterator}, and then call the Iterator's
  +@{tf.data.Iterator.get_next$`get_next`} method.
  The simplest way to create an Iterator is with the
  @{tf.data.Dataset.make_one_shot_iterator$`make_one_shot_iterator`} method.
  @@ -285,6 +286,8 @@ while True:
  break
  ```
  +For more details on Datasets and Iterators see: @{$programmers_guide/datasets}.
  +
  ## Layers
  A trainable model must modify the values in the graph to get new outputs with
  @@ -330,9 +333,8 @@ sess.run(init)
  ```
  Important: Calling `tf.global_variables_initializer` only
  -creates and returns a handle to a TensorFlow operation that will
  -initializes all the global variables when we run it. Until we call `sess.run`,
  -the variables are un-initialized.
  +creates and returns a handle to a TensorFlow operation. That op
  +will initialize all the global variables when we run it with `tf.Session.run`.
  Also note that this `global_variables_initializer` only initializes variables
  that existed in the graph when the initializer was created. So the initializer
  