title: Effect of number of nodes in LSTM
body: <p>I am new to machine learning and I built a neural network with 2 dense layers. When I was experimenting, I had the following observations:</p>
  
  <ol>
  <li><p>When I decreased the number of nodes in each dense layer, I seemed to get better training and prediction accuracy. This was surprising to me because I would assume the more nodes in a dense layer, the more the model can understand the data. Why does decreasing node number improve accuracy?</p></li>
  <li><p>The model also yielded better results when the number of nodes in each dense layer was not consistent. For example, I got the best result when one dense layer had 5 nodes and the other layer had 10, than both layers having 5 nodes or 10 nodes. Why is that? Is it common that inconsistent node counts in the dense layers improve accuracy?</p></li>
  </ol>