update markdown typos in basic retrieval and deep recommender tutorials (#2134)

* Update deep_recommender.py markdown typo

* Update basic_ranking.py typo

* Update basic_ranking.md

* Update deep_recommender.md

* Update deep_recommender.ipynb

* Update basic_ranking.ipynb

Author: yufengg

examples/keras_rs/basic_ranking.py

@@ -64,7 +64,7 @@
 starting at 1 and with no gap. Normally, you would need to create a lookup table
 to map user IDs to integers from 0 to N-1. But as a simplication, we'll use the
 user id directly as an index in our model, in particular to lookup the user
-embedding from the user embedding table. So we need do know the number of users.
+embedding from the user embedding table. So we need to know the number of users.
 """
 
 users_count = (
@@ -78,7 +78,7 @@
 starting at 1 and with no gap. Normally, you would need to create a lookup table
 to map movie IDs to integers from 0 to N-1. But as a simplication, we'll use the
 movie id directly as an index in our model, in particular to lookup the movie
-embedding from the movie embedding table. So we need do know the number of
+embedding from the movie embedding table. So we need to know the number of
 movies.
 """
 

examples/keras_rs/deep_recommender.py

@@ -17,13 +17,15 @@
 usually not be immediately usable in a model.
 
 For example:
+
 - User and item IDs may be strings (titles, usernames) or large, non-contiguous
   integers (database IDs).
 - Item descriptions could be raw text.
 - Interaction timestamps could be raw Unix timestamps.
 
 These need to be appropriately transformed in order to be useful in building
 models:
+
 - User and item IDs have to be translated into embedding vectors,
   high-dimensional numerical representations that are adjusted during training
   to help the model predict its objective better.

examples/keras_rs/ipynb/basic_ranking.ipynb

@@ -104,7 +104,7 @@
     "starting at 1 and with no gap. Normally, you would need to create a lookup table\n",
     "to map user IDs to integers from 0 to N-1. But as a simplication, we'll use the\n",
     "user id directly as an index in our model, in particular to lookup the user\n",
-    "embedding from the user embedding table. So we need do know the number of users."
+    "embedding from the user embedding table. So we need to know the number of users."
    ]
   },
   {
@@ -132,7 +132,7 @@
     "starting at 1 and with no gap. Normally, you would need to create a lookup table\n",
     "to map movie IDs to integers from 0 to N-1. But as a simplication, we'll use the\n",
     "movie id directly as an index in our model, in particular to lookup the movie\n",
-    "embedding from the movie embedding table. So we need do know the number of\n",
+    "embedding from the movie embedding table. So we need to know the number of\n",
     "movies."
    ]
   },
@@ -459,4 +459,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

examples/keras_rs/ipynb/deep_recommender.ipynb

@@ -29,13 +29,15 @@
     "usually not be immediately usable in a model.\n",
     "\n",
     "For example:\n",
+    "\n",
     "- User and item IDs may be strings (titles, usernames) or large, non-contiguous\n",
     "  integers (database IDs).\n",
     "- Item descriptions could be raw text.\n",
     "- Interaction timestamps could be raw Unix timestamps.\n",
     "\n",
     "These need to be appropriately transformed in order to be useful in building\n",
     "models:\n",
+    "\n",
     "- User and item IDs have to be translated into embedding vectors,\n",
     "  high-dimensional numerical representations that are adjusted during training\n",
     "  to help the model predict its objective better.\n",
@@ -1351,4 +1353,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

examples/keras_rs/md/basic_ranking.md

@@ -70,7 +70,7 @@ In the Movielens dataset, user IDs are integers (represented as strings)
 starting at 1 and with no gap. Normally, you would need to create a lookup table
 to map user IDs to integers from 0 to N-1. But as a simplication, we'll use the
 user id directly as an index in our model, in particular to lookup the user
-embedding from the user embedding table. So we need do know the number of users.
+embedding from the user embedding table. So we need to know the number of users.
 
 
 ```python
@@ -85,7 +85,7 @@ In the Movielens dataset, movie IDs are integers (represented as strings)
 starting at 1 and with no gap. Normally, you would need to create a lookup table
 to map movie IDs to integers from 0 to N-1. But as a simplication, we'll use the
 movie id directly as an index in our model, in particular to lookup the movie
-embedding from the movie embedding table. So we need do know the number of
+embedding from the movie embedding table. So we need to know the number of
 movies.
 
 

examples/keras_rs/md/deep_recommender.md

@@ -20,13 +20,15 @@ The first step in doing so is preparing the features, as raw features will
 usually not be immediately usable in a model.
 
 For example:
+
 - User and item IDs may be strings (titles, usernames) or large, non-contiguous
   integers (database IDs).
 - Item descriptions could be raw text.
 - Interaction timestamps could be raw Unix timestamps.
 
 These need to be appropriately transformed in order to be useful in building
 models:
+
 - User and item IDs have to be translated into embedding vectors,
   high-dimensional numerical representations that are adjusted during training
   to help the model predict its objective better.