title: Relationship between over-fitting, gradient vanishing, and local minimum?
body: <p>I'm trying to learn Deep learning.</p>
  
  <p>I'm so confused about these problems: Over-fitting, Gradient vanishing and Local Minimum.</p>
  
  <p>So far, I understood about the RBM which do pre-training because of Over-fitting. But some people saying that pre-training is needed because of Gradient Vanishing, and some other people said because of Local Minima.</p>
  
  <p>So.. question is that, which one is true? If all people saying is correct, could you please tell me about the relationship between those concepts?</p>
  
  <hr>
  
  <p>I’m still confusing. As so far, what I have understand about Gradient Vanishing is that early layers couldn’t training well due to reaching the gradient zero. So what you mean is that early layers that are not training well are same concept as struck at the local minimum? If so, what all layers are well-trained is global optimum. Am I right?</p>
  
  <p>Secondly, in the case of pre-training, I’ve known that it is no longer spotlighted by receiving a lot of data and randomly initialized weights. And receiving a lot of data is related to preventing over-fitting. So..  in this perspective was developed due to preventing over-fitting. Is it wrong idea?</p>