Answers.Body: <p>In general, the higher the model complexity, the more training examples (and computation time) is required for the model to learn.</p>
  <p>In general, network size is directly proportional to model complexity. The larger the network size, the more parameters, thus higher the complexity.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-06-19T04:40:28.040Z
Answers.LastActivityDate: 2023-06-19T04:40:28.040Z
Answers.UserDisplayName: lpounng
Body: <p>What is the relation between any suitable measure of model complexity, number of training examples and network size in deep learning?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-06-16T02:11:50.803Z
Comments.Text: Please name one such measure of model complexity, so we can elaborate from there.
Comments.UserDisplayName: lpounng
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-06-16T09:28:05.970Z
Comments.Text: Let us use VC complexity.
Comments.UserDisplayName: Justaperson
Tags: <deep-learning><training><models><sample-complexity>
Title: What is the relation between any suitable measure of model complexity, number of training examples and network size in deep learning?
UserDisplayName: Justaperson
ThreadText: # What is the relation between any suitable measure of model complexity, number of training examples and network size in deep learning?
  
  (Asked by: username_0 on 2023-06-15)
  
  What is the relation between any suitable measure of model complexity, number of training examples and network size in deep learning?
  
  **Tags:** deep-learning, training, models, sample-complexity
  > **Comments:**
  > **username_1**: Please name one such measure of model complexity, so we can elaborate from there.  
  > **username_0**: Let us use VC complexity.  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2023-06-19. Score: 0
  
  In general, the higher the model complexity, the more training examples (and computation time) is required for the model to learn.
  
  In general, network size is directly proportional to model complexity. The larger the network size, the more parameters, thus higher the complexity.
  
  
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