Fix missing /n in regression.ipynb, nblint, nbfmt

Author: 8bitmp3

site/en/tutorials/keras/regression.ipynb

@@ -290,7 +290,7 @@
         "\n",
         "Now split the dataset into a training set and a test set.\n",
         "\n",
-        "Use the test set in the final evaluation of our models."
+        "Use the test set in the final evaluation of your models."
       ]
     },
     {
@@ -407,7 +407,7 @@
         "\n",
         "One reason this is important is because the features are multiplied by the model weights. So the scale of the outputs and the scale of the gradients are affected by the scale of the inputs. \n",
         "\n",
-        "Although a model *might* converge without feature normalization, normalization makes training much more stable. "
+        "Although a model *might* converge without feature normalization, normalization makes training much more stable.\n",
         "\n",
         "Note: There is no advantage to normalizing the one-hot features, it is done here for simplicity. For more details on how to use the preprocessing layers, refer the [Working with preprocessing layers](https://www.tensorflow.org/guide/keras/preprocessing_layers) guide and the [Classify structured data using Keras preprocessing layers](https://www.tensorflow.org/tutorials/structured_data/preprocessing_layers) tutorial."
       ]