message: Updating references to the `tf.data` API to `tf.data` from `Datasets`.
diff: # Importing Data
  -The @{tf.data.Dataset$`Dataset`} API enables you to build complex input pipelines from
  +The `tf.data` API enables you to build complex input pipelines from
  simple, reusable pieces. For example, the pipeline for an image model might
  aggregate data from files in a distributed file system, apply random
  perturbations to each image, and merge randomly selected images into a batch
  for training. The pipeline for a text model might involve extracting symbols
  from raw text data, converting them to embedding identifiers with a lookup
  -table, and batching together sequences of different lengths. The `Dataset` API
  +table, and batching together sequences of different lengths. The `tf.data` API
  makes it easy to deal with large amounts of data, different data formats, and
  complicated transformations.
  -The `Dataset` API introduces two new abstractions to TensorFlow:
  +The `tf.data` API introduces two new abstractions to TensorFlow:
  * A `tf.data.Dataset` represents a sequence of elements, in which
  each element contains one or more `Tensor` objects. For example, in an image
  @@ -121,7 +121,7 @@ dataset3 = dataset3.filter(lambda x, (y, z): ...)
  ### Creating an iterator
  Once you have built a `Dataset` to represent your input data, the next step is to
  -create an `Iterator` to access elements from that dataset. The `Dataset` API
  +create an `Iterator` to access elements from that dataset. The `tf.data` API
  currently supports the following iterators, in increasing level of
  sophistication:
  @@ -379,7 +379,7 @@ sess.run(iterator.initializer, feed_dict={features_placeholder: features,
  ### Consuming TFRecord data
  -The `Dataset` API supports a variety of file formats so that you can process
  +The `tf.data` API supports a variety of file formats so that you can process
  large datasets that do not fit in memory. For example, the TFRecord file format
  is a simple record-oriented binary format that many TensorFlow applications use
  for training data. The `tf.data.TFRecordDataset` class enables you to
  @@ -628,7 +628,7 @@ TODO(mrry): Add this section.
  ### Processing multiple epochs
  -The `Dataset` API offers two main ways to process multiple epochs of the same
  +The `tf.data` API offers two main ways to process multiple epochs of the same
  data.
  The simplest way to iterate over a dataset in multiple epochs is to use the
  @@ -693,7 +693,7 @@ dataset = dataset.repeat()
  The @{tf.train.MonitoredTrainingSession} API simplifies many aspects of running
  TensorFlow in a distributed setting. `MonitoredTrainingSession` uses the
  @{tf.errors.OutOfRangeError} to signal that training has completed, so to use it
  -with the `Dataset` API, we recommend using
  +with the `tf.data` API, we recommend using
  `Dataset.make_one_shot_iterator()`. For example:
  ```python
  