Unverified Commit d53c3e1d authored by Mark Daoust's avatar Mark Daoust Committed by GitHub
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Merge pull request #4870 from lamberta/text-classify-notebook

Update noteboook title. Add link to MLCC text classification guide.
parents c6bef65a 08564b1f
...@@ -103,7 +103,7 @@ ...@@ -103,7 +103,7 @@
}, },
"cell_type": "markdown", "cell_type": "markdown",
"source": [ "source": [
"# Classify movie reviews: binary classification" "# Text classification with movie reviews"
] ]
}, },
{ {
...@@ -134,11 +134,11 @@ ...@@ -134,11 +134,11 @@
"cell_type": "markdown", "cell_type": "markdown",
"source": [ "source": [
"\n", "\n",
"This notebook classifies movie reviews as *positive* or *negative* using the text of the review. This is an example of binary—or two-class—classification, an important and widely applicable kind of machine learning problem. \n", "This notebook classifies movie reviews as *positive* or *negative* using the text of the review. This is an example of *binary*—or two-class—classification, an important and widely applicable kind of machine learning problem. \n",
"\n", "\n",
"We'll use the [IMDB dataset](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb) that contains the text of 50,000 movie reviews from the [Internet Movie Database](https://www.imdb.com/). These are split into 25,000 reviews for training and 25,000 reviews for testing. The training and testing sets are *balanced*, meaning they contain an equal number of positive and negative reviews. \n", "We'll use the [IMDB dataset](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb) that contains the text of 50,000 movie reviews from the [Internet Movie Database](https://www.imdb.com/). These are split into 25,000 reviews for training and 25,000 reviews for testing. The training and testing sets are *balanced*, meaning they contain an equal number of positive and negative reviews. \n",
"\n", "\n",
"This notebook uses [tf.keras](https://www.tensorflow.org/guide/keras), a high-level API to build and train models in TensorFlow." "This notebook uses [tf.keras](https://www.tensorflow.org/guide/keras), a high-level API to build and train models in TensorFlow. For a more advanced text classification tutorial using `tf.keras`, see the [MLCC Text Classification Guide](https://developers.google.com/machine-learning/guides/text-classification/)."
] ]
}, },
{ {
...@@ -813,4 +813,4 @@ ...@@ -813,4 +813,4 @@
] ]
} }
] ]
} }
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