title: Word Embedding, LookupTable, Word Embedding Visualizations
body: <p>I need to ask few questions regarding word embeddings.....could be basic.</p>
  
  <ol>
  <li>When we convert a one-hot vector of a word for instance king <code>[0 0 0 1 0]</code> into an embedded vector <code>E = [0.2, 0.4, 0.2, 0.2]</code>.... is there any importance for each index in resultant word vector? For instance <code>E[1]</code> which is 0.2.... what specifically <code>E[1]</code> defines (although I know its basically a transformation into another space).... or word vector collectively defines context but not individually...</li>
  <li>How the dimension (reduced or increased) of a word vector matters as compared to the original one-hot vector ?</li>
  <li>How can we define lookup table in term of embedding layer?</li>
  <li>is lookup table a kind of random generated table or it already been trained separately with respect to data instance in data and we just use it later on in Neural Network operations?
  5- Is there any method to visualize an embedded vector at Hidden Layer (as we do have in Image based Neural Network Processing)?</li>
  </ol>
  
  <p>Thanks in advance</p>