Answers.Body: <p>Updating model for each training example means batch size of 1, aka stochastic gradient descent(SGD).</p>
  <p>1 iteration is defined as forward propagate, calculate loss, backpropagate and finally update weights.</p>
  <p>Since batch size is 1, running 5 epochs on 100 training examples with SGD means you will do 500 iterations, yes.</p>
  
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Answers.CreationDate: 2021-07-25T15:50:01.937Z
Answers.LastActivityDate: 2021-07-25T15:50:01.937Z
Answers.UserDisplayName: meliksahturker
Body: <p>Per <a href="https://developers.google.com/machine-learning/glossary#iteration" rel="nofollow noreferrer">google machine-learning glossary</a>, when I have 100 training examples and update my model for each training example, if I train my model 5 epochs without early-stop, there are 500 iterations in total, is my understanding correct?</p>
  
Tags: <terminology>
Title: Is my understanding about the number of iterations correct?
UserDisplayName: JJJohn
ThreadText: # Is my understanding about the number of iterations correct?
  
  (Asked by: username_0 on 2021-07-25)
  
  Per [google machine-learning glossary](https://developers.google.com/machine-learning/glossary#iteration), when I have 100 training examples and update my model for each training example, if I train my model 5 epochs without early-stop, there are 500 iterations in total, is my understanding correct?
  
  **Tags:** terminology
  
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  ## 1 answers
  
  ### Answer by username_1 on 2021-07-25. Score: 1 | Accepted answer
  
  Updating model for each training example means batch size of 1, aka stochastic gradient descent(SGD).
  
  1 iteration is defined as forward propagate, calculate loss, backpropagate and finally update weights.
  
  Since batch size is 1, running 5 epochs on 100 training examples with SGD means you will do 500 iterations, yes.
  
  
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