title: Avoiding Dummy variable trap and neural network
body: <p>I know that categorical data should be one-hot encoded before training the machine learning algorithm. I also need that for multivariate linear regression I need to exclude one of the encoded variable to avoid so called dummy variable trap.</p>
  
  <p>Ex: If I have categorical feature "size": "small", "medium", "large", then in one hot encoded I would have something like:</p>
  
  <pre><code>small  medium  large other-feature
    0      1       0      2999
  </code></pre>
  
  <p>So to avoid dummy variable trap I need to remove any of the 3 columns, for example, column "small".</p>
  
  <p>Should I do the same for training a Neural Network? Or this is purely for multivariate regression?</p>
  
  <p>Thanks.</p>