Answers.Body: <p>The width refers to the number of neurons in a layer. The depth refers to the number of layers. </p>
  
  <p>Have a look at the following question regarding the impact of these hyper-parameters on the performance of the neural network: <a href="https://stats.stackexchange.com/q/214360/82135">https://stats.stackexchange.com/q/214360/82135</a>.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-25T22:11:24.217Z
Answers.LastActivityDate: 2019-03-25T22:11:24.217Z
Answers.UserDisplayName: nbro
Body: <p>From this <a href="http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/" rel="nofollow noreferrer">article</a>, I read that "to accurately classify data with neural networks, <strong>wide</strong> layers are sometimes necessary."</p>
  
  <p>However, I have seen many implementations and discussions on deep-learning, such as <a href="https://skymind.ai/wiki/neural-network" rel="nofollow noreferrer">this</a>, mention the concept of <strong>depth</strong>.  </p>
  
  <p>What is the difference in the context of neural networks?  How does width vs depth impact a neural network's performance?</p>
  
Tags: <neural-networks><deep-learning>
Title: What does "Wide" vs. "Deep" mean in the context of Neural Networks?
UserDisplayName: SeeDerekEngineer
ThreadText: # What does "Wide" vs. "Deep" mean in the context of Neural Networks?
  
  (Asked by: username_0 on 2019-03-25)
  
  From this [article](http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/), I read that "to accurately classify data with neural networks, **wide** layers are sometimes necessary."
  
  However, I have seen many implementations and discussions on deep-learning, such as [this](https://skymind.ai/wiki/neural-network), mention the concept of **depth**.
  
  What is the difference in the context of neural networks? How does width vs depth impact a neural network's performance?
  
  **Tags:** neural-networks, deep-learning
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-03-25. Score: 6 | Accepted answer
  
  The width refers to the number of neurons in a layer. The depth refers to the number of layers.
  
  Have a look at the following question regarding the impact of these hyper-parameters on the performance of the neural network: <https://stats.stackexchange.com/q/214360/82135>.
  
  
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