message: Fix errors and typos in the Estimators programmer's guide
diff: @@ -21,18 +21,17 @@ Note: TensorFlow also includes a deprecated `Estimator` class at
  Estimators provide the following benefits:
  -* You can run Estimators-based models on a local host or on a
  +* You can run Estimator-based models on a local host or on a
  distributed multi-server environment without changing your model.
  - Furthermore, you can run Estimators-based models on CPUs, GPUs,
  + Furthermore, you can run Estimator-based models on CPUs, GPUs,
  or TPUs without recoding your model.
  * Estimators simplify sharing implementations between model developers.
  -* You can develop a state of the art model with high-level intuitive code,
  +* You can develop a state of the art model with high-level intuitive code.
  In short, it is generally much easier to create models with Estimators
  than with the low-level TensorFlow APIs.
  -* Estimators are themselves built on tf.layers, which
  +* Estimators are themselves built on @{tf.layers}, which
  simplifies customization.
  -* Estimators build the graph for you. In other words, you don't have to
  - build the graph.
  +* Estimators build the graph for you.
  * Estimators provide a safe distributed training loop that controls how and
  when to:
  * build the graph
  @@ -57,7 +56,7 @@ the "plumbing" for you. That is, pre-made Estimators create and manage
  pre-made Estimators let you experiment with different model architectures by
  making only minimal code changes. @{tf.estimator.DNNClassifier$`DNNClassifier`},
  for example, is a pre-made Estimator class that trains classification models
  -through dense, feed-forward neural networks.
  +based on dense, feed-forward neural networks.
  ### Structure of a pre-made Estimators program
  @@ -79,7 +78,7 @@ of the following four steps:
  an input function:
  def input_fn(dataset):
  - ... # manipulate dataset, extracting feature names and the label
  + ... # manipulate dataset, extracting the feature dict and the label
  return feature_dict, label
  (See @{$programmers_guide/datasets} for full details.)
  @@ -96,13 +95,13 @@ of the following four steps:
  population = tf.feature_column.numeric_column('population')
  crime_rate = tf.feature_column.numeric_column('crime_rate')
  median_education = tf.feature_column.numeric_column('median_education',
  - normalizer_fn='lambda x: x - global_education_mean')
  + normalizer_fn=lambda x: x - global_education_mean)
  3. **Instantiate the relevant pre-made Estimator.** For example, here's
  a sample instantiation of a pre-made Estimator named `LinearClassifier`:
  # Instantiate an estimator, passing the feature columns.
  - estimator = tf.estimator.Estimator.LinearClassifier(
  + estimator = tf.estimator.LinearClassifier(
  feature_columns=[population, crime_rate, median_education],
  )
  