Update site/en/tutorials/keras/regression.ipynb

Author: 8bitmp3

site/en/tutorials/keras/regression.ipynb

@@ -1368,7 +1368,7 @@
         "\n",
         "This notebook introduced a few techniques to handle a regression problem. Here are a few more tips that may help:\n",
         "\n",
-        "- Mean squared error (MSE) (`tf.keras.losses.MeanSquaredError`) and mean absolute error (MAE) (`tf.losses.MeanAbsoluteError`) are common loss functions used for regression problems. MAE is less sensitive to outliers. Different loss functions are used for classification problems.\n",
+        "- Mean squared error (MSE) (`tf.keras.losses.MeanSquaredError`) and mean absolute error (MAE) (`tf.keras.losses.MeanAbsoluteError`) are common loss functions used for regression problems. MAE is less sensitive to outliers. Different loss functions are used for classification problems.\n",
         "- Similarly, evaluation metrics used for regression differ from classification.\n",
         "- When numeric input data features have values with different ranges, each feature should be scaled independently to the same range.\n",
         "- Overfitting is a common problem for DNN models, though it wasn't a problem for this tutorial. Visit the [Overfit and underfit](overfit_and_underfit.ipynb) tutorial for more help with this."