title: One-Hot-Encoding for Wide&Deep-Model and normalization by using DNNLinearCombinedClassifier
body: <p>I'm starting to use Tensorflow and read <a href="https://arxiv.org/abs/1606.07792" rel="nofollow noreferrer">the Whitepaper of <em>Wide&amp;Deep Learning for Recommender Systems</em> from Cheng et al.</a> Now I have questions in two domains by using it with tensorflow like it's done in <a href="https://www.tensorflow.org/versions/r0.11/tutorials/wide_and_deep/index.html" rel="nofollow noreferrer">the tutorial</a>. </p>
  
  <ol>
  <li><p>If I use categorial Columns like <code>tf.contrib.layers.sparse_column_with_hash_bucket("education", hash_bucket_size=1000)</code>: Uses the model a <strong>One-Hot-Encoding</strong> for this feature in its back? Or can I apply a One-hot-Encoding and are there advantages of this hashing instead of One-Hot-Encoding? What's about the Hash: Do I have to set the <code>hash_bucket_size</code>to a value as high as distinct values of this feature?</p></li>
  <li><p>The second question is about <em>normalization</em> of continous real-valued-features. Cheng et al. wrote that their "Continuous real-valued features are normalized to [0,1]". If I use the <code>tf.contrib.learn.DNNLinearCombinedClassifier</code> will this be done automatically? Or is it up to me to implement these normalizations?</p></li>
  </ol>
  
  <p>Thanks for your support!</p>