message: Modified DeepFM sample's REAMDE.md to describe data preprocessing
diff: @@ -5,17 +5,23 @@ A sample of building and training DeepFM Network with HugeCTR [(link)](https://w
  The data is provided by CriteoLabs (http://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset/).
  The original training set contains 45,840,617 examples.
  Each example contains a label (1 if the ad was clicked, otherwise 0) and 39 features (13 integer features and 26 categorical features).
  -Because the original test set doesn't contain labels, it is not used.
  +The dataset also has the significant amounts of missing values across the feature columns, which should be preprocessed acordingly.
  +The original test set doesn't contain labels, so it's not used.
  ### Requirements ###
  -Python >= 3.6.9
  -Pandas 1.0.1
  -Sklearn 0.22.1
  +* Python >= 3.6.9
  +* Pandas 1.0.1
  +* Sklearn 0.22.1
  1. Download the dataset and preprocess
  Go to [(link)](http://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset/)
  and download kaggle-display dataset into the folder "${project_home}/tools/criteo_script/".
  +The script `preprocess.sh` fills the missing values by mapping them to the unusned unique integer or category.
  +It also replaces unique values which appear less than six times across the entire dataset with the unique value for missing values.
  +Its purpose is to redcue the vocabulary size of each columm while not losing too much information.
  +In addition, it normalizes the integer feature values to the range [0, 1],
  +but it doesn't create any feature crosses.
  ```shell
  # The preprocessing can take 1-4 hours based on the system configuration.
  