fix formatting error in the docs for entity embedding

Author: ablaom

docs/src/common_workflows/entity_embeddings/notebook.ipynb

@@ -9,7 +9,7 @@
 # employed in NLP architectures.
 
 # In MLJFlux, the `NeuralNetworkClassifier`, `NeuralNetworkRegressor`, and the
-# `MultitargetNeuralNetworkRegressor`` can be trained and evaluated with heterogenous data
+# `MultitargetNeuralNetworkRegressor` can be trained and evaluated with heterogenous data
 # (i.e., containing categorical features) because they have a built-in entity embedding
 # layer.  Moreover, they offer a `transform` method which encodes the categorical features
 # with the learned embeddings. Such embeddings can then be used as features in downstream

docs/src/common_workflows/entity_embeddings/notebook.jl

@@ -13,7 +13,7 @@ categorical feature into a dense continuous vector in a similar fashion to how t
 employed in NLP architectures.
 
 In MLJFlux, the `NeuralNetworkClassifier`, `NeuralNetworkRegressor`, and the
-`MultitargetNeuralNetworkRegressor`` can be trained and evaluated with heterogenous data
+`MultitargetNeuralNetworkRegressor` can be trained and evaluated with heterogenous data
 (i.e., containing categorical features) because they have a built-in entity embedding
 layer.  Moreover, they offer a `transform` method which encodes the categorical features
 with the learned embeddings. Such embeddings can then be used as features in downstream

docs/src/common_workflows/entity_embeddings/notebook.md

@@ -29,7 +29,7 @@
    "cell_type": "markdown",
    "source": [
     "In MLJFlux, the `NeuralNetworkClassifier`, `NeuralNetworkRegressor`, and the\n",
-    "`MultitargetNeuralNetworkRegressor`` can be trained and evaluated with heterogenous data\n",
+    "`MultitargetNeuralNetworkRegressor` can be trained and evaluated with heterogenous data\n",
     "(i.e., containing categorical features) because they have a built-in entity embedding\n",
     "layer.  Moreover, they offer a `transform` method which encodes the categorical features\n",
     "with the learned embeddings. Such embeddings can then be used as features in downstream\n",