Lint Migration examples: Canned Estimators guide

Author: 8bitmp3

site/en/guide/migrate/canned_estimators.ipynb

@@ -37,7 +37,7 @@
         "id": "77z2OchJTk0l"
       },
       "source": [
-        "# Migration Examples: Canned Estimators\n",
+        "# Migration examples: Canned Estimators\n",
         "\n",
         "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
         "  <td>\n",
@@ -67,7 +67,7 @@
       "source": [
         "Canned (or Premade) Estimators have traditionally been used in TensorFlow 1 as quick and easy ways to train models for a variety of typical use cases. TensorFlow 2 provides straightforward approximate substitutes for a number of them by way of Keras models. For those canned estimators that do not have built-in TensorFlow 2 substitutes, you can still build your own replacement fairly easily.\n",
         "\n",
-        "This guide walks through a few examples of direct equivalents and custom substitutions to demonstrate how TensorFlow 1's `tf.estimator`-derived models can be migrated to TF2 with Keras.\n",
+        "This guide will walk you through a few examples of direct equivalents and custom substitutions to demonstrate how TensorFlow 1's `tf.estimator`-derived models can be migrated to TensorFlow 2 with Keras.\n",
         "\n",
         "Namely, this guide includes examples for migrating:\n",
         "*    From `tf.estimator`'s `LinearEstimator`, `Classifier` or `Regressor` in TensorFlow 1 to Keras `tf.compat.v1.keras.models.LinearModel` in TensorFlow 2\n",
@@ -183,7 +183,7 @@
         "id": "bYSgoezeMrpI"
       },
       "source": [
-        "and create a method to instantiate a simplistic sample optimizer to use with our various TensorFlow 1 Estimator and TensorFlow 2 Keras models."
+        "and create a method to instantiate a simplistic sample optimizer to use with various TensorFlow 1 Estimator and TensorFlow 2 Keras models."
       ]
     },
     {
@@ -226,7 +226,7 @@
         "id": "_O7fyhCnpvED"
       },
       "source": [
-        "### TF1: Using LinearEstimator"
+        "### TensorFlow 1: Using LinearEstimator"
       ]
     },
     {
@@ -270,7 +270,7 @@
         "id": "KEmzBjfnsxwT"
       },
       "source": [
-        "### TF2: Using Keras LinearModel"
+        "### TensorFlow 2: Using Keras LinearModel"
       ]
     },
     {
@@ -311,7 +311,7 @@
         "id": "YKl6XZ7Bp1t5"
       },
       "source": [
-        "### TF1: Using DNNEstimator"
+        "### TensorFlow 1: Using DNNEstimator"
       ]
     },
     {
@@ -320,7 +320,7 @@
         "id": "J7wJUmgypln8"
       },
       "source": [
-        "In TensorFlow 1, you can use `tf.estimator.DNNEstimator` to create a baseline DNN model for regression and classification problems."
+        "In TensorFlow 1, you can use `tf.estimator.DNNEstimator` to create a baseline deep neural network (DNN) model for regression and classification problems."
       ]
     },
     {
@@ -357,7 +357,7 @@
         "id": "6xJz6px6pln-"
       },
       "source": [
-        "### TF2: Using Keras to Create a Custom DNN Model"
+        "### TensorFlow 2: Using Keras to create a custom DNN model"
       ]
     },
     {
@@ -368,7 +368,7 @@
       "source": [
         "In TensorFlow 2, you can create a custom DNN model to substitute for one generated by `tf.estimator.DNNEstimator`, with similar levels of user-specified customization (for instance, as in the previous example, the ability to customize a chosen model optimizer).\n",
         "\n",
-        "A similar workflow can be used to replace `tf.estimator.experimental.RNNEstimator` with a Keras RNN Model. Keras provides a number of built-in, customizable choices by way of `tf.keras.layers.RNN`, `tf.keras.layers.LSTM`, and `tf.keras.layers.GRU` - see [here](https://www.tensorflow.org/guide/keras/rnn#built-in_rnn_layers_a_simple_example) for more details."
+        "A similar workflow can be used to replace `tf.estimator.experimental.RNNEstimator` with a Keras recurrent neural network (RNN) model. Keras provides a number of built-in, customizable choices by way of `tf.keras.layers.RNN`, `tf.keras.layers.LSTM`, and `tf.keras.layers.GRU`. To learn more, check out the _Built-in RNN layers: a simple example_ section of [RNN with Keras guide](https://www.tensorflow.org/guide/keras/rnn)."
       ]
     },
     {
@@ -413,7 +413,7 @@
         "id": "GfRaObf5g4TU"
       },
       "source": [
-        "### TF1: Using DNNLinearCombinedEstimator"
+        "### TensorFlow 1: Using DNNLinearCombinedEstimator"
       ]
     },
     {
@@ -464,7 +464,7 @@
         "id": "BeMikL5ug4TX"
       },
       "source": [
-        "### TF2: Using Keras WideDeepModel"
+        "### TensorFlow 2: Using Keras WideDeepModel"
       ]
     },
     {
@@ -477,7 +477,7 @@
         "\n",
         "This `WideDeepModel` is constructed on the basis of a constituent `LinearModel` and a custom DNN Model, both of which are discussed in the preceding two examples. A custom linear model can also be used in place of the built-in Keras `LinearModel` if desired.\n",
         "\n",
-        "If you would like to build your own model instead of a canned estimator, check out [how to build a `keras.Sequential` model](https://www.tensorflow.org/guide/keras/sequential_model). For more information on custom training and optimizers you can also checkout [this guide](https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough)."
+        "If you would like to build your own model instead of using a canned estimator, check out the [Keras Sequential model](https://www.tensorflow.org/guide/keras/sequential_model) guide. For more information on custom training and optimizers, check out the [Custom training: walkthrough](https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough) guide."
       ]
     },
     {
@@ -532,7 +532,7 @@
         "id": "_3mCQVDSeOKD"
       },
       "source": [
-        "### TF1: Using BoostedTreesEstimator"
+        "### TensorFlow 1: Using BoostedTreesEstimator"
       ]
     },
     {
@@ -578,7 +578,7 @@
         "id": "eNuLP6BeeOKF"
       },
       "source": [
-        "### TF2: Using TensorFlow Decision Forests"
+        "### TensorFlow 2: Using TensorFlow Decision Forests"
       ]
     },
     {
@@ -689,9 +689,9 @@
         "id": "Z22UJ5SUqToQ"
       },
       "source": [
-        "Gradient Boosted Trees is just one of the many decision forests algorithms available in TensorFlow Decision Forests. For example, Random Forests (available as [tfdf.keras.GradientBoostedTreesModel](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/RandomForestModel) is very resistant to overfitting) while CART (available as [tfdf.keras.CartModel](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/CartModel)) is great for model interpretation.\n",
+        "Gradient Boosted Trees is just one of the many decision forest algorithms available in TensorFlow Decision Forests. For example, Random Forests (available as [tfdf.keras.GradientBoostedTreesModel](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/RandomForestModel) is very resistant to overfitting) while CART (available as [tfdf.keras.CartModel](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/CartModel)) is great for model interpretation.\n",
         "\n",
-        "In the next example, we train and plot a Random Forest model."
+        "In the next example, train and plot a Random Forest model."
       ]
     },
     {
@@ -718,7 +718,7 @@
         "id": "Z0QYolhoZb_k"
       },
       "source": [
-        "Finally, in the next example, we train and evaluate a CART model."
+        "In the final example, train and evaluate a CART model."
       ]
     },
     {