title: Neural Network Categorical Data Implementation
body: <p>I've been learning to work with neural networks as a hobby project, but am at a complete loss with how to handle categorical data. I read the article <a href="http://visualstudiomagazine.com/articles/2013/07/01/neural-network-data-normalization-and-encoding.aspx" rel="noreferrer">http://visualstudiomagazine.com/articles/2013/07/01/neural-network-data-normalization-and-encoding.aspx</a>, which explains normalization of the input data and explains how to preprocess categorical data using effects encoding. I understand the concept of breaking the categories into vectors, but have no idea how to actually implement this.</p>
  
  <p>For example, if I'm using countries as categorical data (e.g. Finland, Thailand, etc), would I process the resulting vector into a single number to be fed to a single input, or would I have a separate input for each component of the vector? Under the latter, if there are 196 different countries, that would mean I would need 196 different inputs just to process this particular piece of data. If a lot of different categorical data is being fed to the network, I can see this becoming really unwieldy very fast.</p>
  
  <p>Is there something I'm missing? How exactly is categorical data mapped to neuron inputs?</p>