The embedding column example had a comment that seemed to incorrectly relate to the indicator column. Updated the comment to reflect the embedding column info. Also updated the name of the dimension variable to be consistent with the example from a few lines earlier.
categorical_column = ... # Create any categorical column
# Represent the categorical column as an embedding column.
-# This means creating a one-hot vector with one element for each category.
+# This means creating an embedding vector lookup table with one element for each category.
embedding_column = tf.feature_column.embedding_column(
categorical_column=categorical_column,
- dimension=dimension_of_embedding_vector)
+ dimension=embedding_dimensions)
```
@{$programmers_guide/embedding$Embeddings} is a significant topic within machine