Body: <p>Suppose there is binary classification problem, that's mean the output is scalar.</p>
  <p>Suppose the input is vector that accepts 512 elements.</p>
  <p>First model is trained for preprocessor, and the second one for main goal.</p>
  <p>First model input size 512 and outpyt size 512.</p>
  <p>Secobd model input size 512 and output size is 1.</p>
  <p>So, is it considered as end to end?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-07-11T20:51:54.137Z
Comments.Text: end to end just means that you have no active supervision during the forward training process, but you might definitely have more than one step
Comments.UserDisplayName: Alberto
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-07-11T21:23:48.727Z
Comments.Text: @Alberto I see, it's still not considered as end-to-end learning. Because there are two inference processes.
Comments.UserDisplayName: Muhammad Ikhwan Perwira
Tags: <deep-learning><data-preprocessing>
Title: Is training two models of deep learning is considered as end-to-end learning?
UserDisplayName: Muhammad Ikhwan Perwira
ThreadText: # Is training two models of deep learning is considered as end-to-end learning?
  
  (Asked by: username_0 on 2024-07-11)
  
  Suppose there is binary classification problem, that's mean the output is scalar.
  
  Suppose the input is vector that accepts 512 elements.
  
  First model is trained for preprocessor, and the second one for main goal.
  
  First model input size 512 and outpyt size 512.
  
  Secobd model input size 512 and output size is 1.
  
  So, is it considered as end to end?
  
  **Tags:** deep-learning, data-preprocessing
  
  > **Comments:**
  > **username_1**: end to end just means that you have no active supervision during the forward training process, but you might definitely have more than one step  
  > **username_0**: @username_1 I see, it's still not considered as end-to-end learning. Because there are two inference processes.  
  
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  ## 0 answers
  
  