message: Change "Datasets" to "`tf.data`" in the "Reading Data" API guide.
diff: # Reading data
  Note: The preferred way to feed data into a tensorflow program is using the
  -@{$datasets$Datasets API}.
  +@{$datasets$`tf.data` API}.
  There are four methods of getting data into a TensorFlow program:
  -* `Dataset` API: Easily construct a complex input pipeline. (preferred method)
  +* `tf.data` API: Easily construct a complex input pipeline. (preferred method)
  * Feeding: Python code provides the data when running each step.
  * `QueueRunner`: a queue-based input pipeline reads the data from files
  at the beginning of a TensorFlow graph.
  @@ -14,26 +14,27 @@ There are four methods of getting data into a TensorFlow program:
  [TOC]
  -## Dataset API
  +## `tf.data` API
  See the @{$datasets$programmer's guide} for an in-depth explanation of
  -@{tf.data.Dataset}. The `Dataset` API allows you to extract and preprocess data
  -from different input/file formats, and apply transformations such as batch,
  -shuffle, and map to the dataset. This is an improved version of the old input
  -methods, feeding and `QueueRunner`.
  +@{tf.data.Dataset}. The `tf.data` API enables you to extract and preprocess data
  +from different input/file formats, and apply transformations such as batching,
  +shuffling, and mapping functions over the dataset. This is an improved version
  +of the old input methods---feeding and `QueueRunner`---which are described
  +below for historical purposes.
  ## Feeding
  +Warning: "Feeding" is the least efficient way to feed data into a TensorFlow
  +program and should only be used for small experiments and debugging.
  +
  TensorFlow's feed mechanism lets you inject data into any Tensor in a
  -computation graph. A python computation can thus feed data directly into the
  +computation graph. A Python computation can thus feed data directly into the
  graph.
  Supply feed data through the `feed_dict` argument to a run() or eval() call
  that initiates computation.
  -Warning: "Feeding" is the least efficient way to feed data into a tensorflow
  -program and should only be used for small experiments and debugging.
  -
  ```python
  with tf.Session():
  input = tf.placeholder(tf.float32)
  @@ -55,6 +56,10 @@ and is described in the @{$mechanics$MNIST tutorial}.
  ## `QueueRunner`
  +Warning: This section discusses implementing input pipelines using the
  +queue-based APIs which can be cleanly replaced by the @{$datasets$`tf.data`
  +API}.
  +
  A typical queue-based pipeline for reading records from files has the following stages:
  1. The list of filenames
  @@ -66,9 +71,6 @@ A typical queue-based pipeline for reading records from files has the following
  7. *Optional* preprocessing
  8. Example queue
  -Warning: This section discusses implementing input pipelines using the
  -queue-based APIs which can be cleanly replaced by the @{$datasets$Datasets API}.
  -
  ### Filenames, shuffling, and epoch limits
  For the list of filenames, use either a constant string Tensor (like
  @@ -499,7 +501,7 @@ You can have the train and eval in the same graph in the same process, and share
  their trained variables or layers. See @{$variables$the shared variables tutorial}.
  To support the single-graph approach
  -@{$programmers_guide/datasets$Datasets} also supplies
  +@{$programmers_guide/datasets$`tf.data`} also supplies
  @{$programmers_guide/datasets#creating_an_iterator$advanced iterator types} that
  that allow the user to change the input pipeline without rebuilding the graph or
  session.
  