added more information on the wide path and the deep path

README.md

@@ -14,9 +14,15 @@ metrics:
 
 A wide & deep neural network trained on the California Housing dataset.
 
-It takes eight inputs: `'MedInc'`, `'HouseAge'`, `'AveRooms'`, `'AveBedrms'`, `'Population'`, `'AveOccup'`, `'Latitude'` and `'Longitude'`. It predicts `'MedHouseVal'`.
+It takes eight features: `'MedInc'`, `'HouseAge'`, `'AveRooms'`, `'AveBedrms'`, `'Population'`, `'AveOccup'`, `'Latitude'` and `'Longitude'`. It predicts `'MedHouseVal'`.
 
-It is a PyTorch adaptation of the TensorFlow model in Chapter 10 of Aurelien Geron's book 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'.
+The first five features (`'MedInc'`, `'HouseAge'`, `'AveRooms'`, `'AveBedrms'` and `'Population'`) flow through the wide path.
+
+The last six features (`'AveRooms'`, `'AveBedrms'`, `'Population'`, `'AveOccup'`, `'Latitude'` and `'Longitude'`) flow through the deep path.
+
+Note: The features `'AveRooms'`, `'AveBedrms'` and `'Population'` flow through both the wide path and the deep path.
+
+This model is a PyTorch adaptation of the TensorFlow model in Chapter 10 of Aurelien Geron's book 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'.
 
 ![](https://raw.githubusercontent.com/sambitmukherjee/handson-ml3-pytorch/main/chapter10/Figure_10-14.png)