Answers.Body: <p><em>Yes, firstly you are correct to acknowledge the term node with reference to the neural network.</em></p>
  
  <p>A neural network is made to mimic the working of a human brain hence the term neural. It comprises of several layers and each layer comprises of several nodes. A network can be a deep neural network, signifying the presence of numerous layers, in which the output of the one, serves as an input for the other.
  Further a node in a particular layer has some weights and biases attached with it that are responsible for the computation and producing output.</p>
  
  <p><strong>To your context, a heavy node may refer</strong> to a node that holds importance for the network and has got significant weight and bias attached. Henceforth as per the statement if the network does make any significant change to the node with importance in terms of weights and biases, it is noticed else not.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-01-16T16:31:57.280Z
Answers.LastActivityDate: 2018-01-16T16:31:57.280Z
Answers.UserDisplayName: Saurav Joshi
Body: <p>I was watching a documentary on Netflix about AlphaGo, and at one point (~1:10:16 from the end), one of the programmers uses the term "heavy node," which I assume has to do with neural networks. I did a little bit of research but couldn't find anything on what that term means. The closest I could get was this wikipedia page on Heavy path decomposition: <a href="https://en.wikipedia.org/wiki/Heavy_path_decomposition" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Heavy_path_decomposition</a>, which seemed like it could be somewhat related, but I wasn't sure how exactly. Has anyone heard of this term being used? Does anyone know what it means? </p>
  
  <p>For context, in the documentary the line is that if it (the network/player) creates something new not in the heavy node, then they don't see it. </p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-01-15T19:45:43.033Z
Comments.Text: I'm not aware of what a heavy node is. I remember watching this movie and that term didn't catch my attention. If you're interested in some of the inner workings of AlphaGo, you could check out their latest paper -
  http://www.mooc360.com/agz_unformatted_nature.pdf (note this is AlphaGo Zero - the successor to AlphaGo featured in Netflix)
Comments.UserDisplayName: Zakk Diaz
Tags: <neural-networks><deep-learning>
Title: What is a heavy node in neural networks?
UserDisplayName: Harpagornis
ThreadText: # What is a heavy node in neural networks?
  
  (Asked by: username_0 on 2018-01-14)
  
  I was watching a documentary on Netflix about AlphaGo, and at one point (~1:10:16 from the end), one of the programmers uses the term "heavy node," which I assume has to do with neural networks. I did a little bit of research but couldn't find anything on what that term means. The closest I could get was this wikipedia page on Heavy path decomposition: <https://en.wikipedia.org/wiki/Heavy_path_decomposition>, which seemed like it could be somewhat related, but I wasn't sure how exactly. Has anyone heard of this term being used? Does anyone know what it means?
  
  For context, in the documentary the line is that if it (the network/player) creates something new not in the heavy node, then they don't see it.
  
  **Tags:** neural-networks, deep-learning
  
  > **Comments:**
  > **username_1**: I'm not aware of what a heavy node is. I remember watching this movie and that term didn't catch my attention. If you're interested in some of the inner workings of AlphaGo, you could check out their latest paper - http://www.mooc360.com/agz_unformatted_nature.pdf (note this is AlphaGo Zero - the successor to AlphaGo featured in Netflix)  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2018-01-16. Score: 4 | Accepted answer
  
  *Yes, firstly you are correct to acknowledge the term node with reference to the neural network.*
  
  A neural network is made to mimic the working of a human brain hence the term neural. It comprises of several layers and each layer comprises of several nodes. A network can be a deep neural network, signifying the presence of numerous layers, in which the output of the one, serves as an input for the other.
  Further a node in a particular layer has some weights and biases attached with it that are responsible for the computation and producing output.
  
  **To your context, a heavy node may refer** to a node that holds importance for the network and has got significant weight and bias attached. Henceforth as per the statement if the network does make any significant change to the node with importance in terms of weights and biases, it is noticed else not.
  
  
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