Answers.Body: <p>Formally, a single hidden layer is sufficient to approximate a continuous function to any desired degree of accuracy, so in that sense, you never need more than 1. This is called the <a href="https://en.wikipedia.org/wiki/Universal_approximation_theorem" rel="noreferrer">Universal Approximation Theorem</a>.</p>
  
  <p>Finding the best topology for a given problem is an open research problem. As far as I know, there are few universal 'rules of thumb' for this.</p>
  
  <p>For a given problem, one option is to apply a <em>neuroevolutionary</em> approach such as <a href="https://www.cs.ucf.edu/~kstanley/neat.html" rel="noreferrer">NEAT</a>, which attempts to find a topology that works well for the problem at hand.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2016-08-03T10:26:17.493Z
Answers.LastActivityDate: 2017-11-05T17:58:56.897Z
Answers.LastEditDate: 2017-11-05T17:58:56.897Z
Answers.UserDisplayName: NietzscheanAI
Body: <p>I've read that the most of the problems can be solved with 1-2 hidden layers.</p>
  
  <p>How do you know you need more than 2? For what kind of problems you would need them (give me an example)?</p>
  
Tags: <deep-neural-networks><hidden-layers>
Title: What kind of problems require more than 2 hidden layers?
UserDisplayName: kenorb
ThreadText: # What kind of problems require more than 2 hidden layers?
  
  (Asked by: username_0 on 2016-08-02)
  
  I've read that the most of the problems can be solved with 1-2 hidden layers.
  
  How do you know you need more than 2? For what kind of problems you would need them (give me an example)?
  
  **Tags:** deep-neural-networks, hidden-layers
  
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  ## 1 answers
  
  ### Answer by username_1 on 2016-08-03. Score: 12 | Accepted answer
  
  Formally, a single hidden layer is sufficient to approximate a continuous function to any desired degree of accuracy, so in that sense, you never need more than 1. This is called the [Universal Approximation Theorem](https://en.wikipedia.org/wiki/Universal_approximation_theorem).
  
  Finding the best topology for a given problem is an open research problem. As far as I know, there are few universal 'rules of thumb' for this.
  
  For a given problem, one option is to apply a *neuroevolutionary* approach such as [NEAT](https://www.cs.ucf.edu/~kstanley/neat.html), which attempts to find a topology that works well for the problem at hand.
  
  
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