title: Normalize Machine Learning Inputs
body: <p>I have a set of inputs that has 5000ish features with values that vary from 0.005 to 9000000. Each of the features has similar values (a feature with a value of 10ish will not also have a value of 0.1ish)</p>
  
  <p>I am trying to apply linear regression to this data set, however, the wide range of input values is inhibiting effective gradient descent.</p>
  
  <p>What is the best way to handle this variance? If normalization is best, please include details on the best way to implement this normalization. </p>
  
  <p>Thanks!</p>