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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
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examples/keras_rs/basic_ranking.py

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

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

examples/keras_rs/ipynb/basic_ranking.ipynb

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

examples/keras_rs/ipynb/deep_recommender.ipynb

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

examples/keras_rs/md/basic_ranking.md

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

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

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