Body: <p>Multi-Layer Perceptron (MLP), Deep AutoEncoder (DAE), and Deep Belief Network (DBN) are trained differently.</p>
  <p>However, do they follow the same process during the inference phase, i.e., do they calculate a weighted sum, then apply a non-linear activation function, for each layer until the last layer, or is there any difference? Moreover, are they only composed of fully connected layers?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-04-05T02:18:04.337Z
Comments.Text: Could you please focus on the comparison of 2 models at a time rather than 3? Moreover, when you use an acronym, like DAE, you should first use the long/full version, like "muli-layer perceptron (MLP)", so that everyone knows what the acronym refers to.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-04-05T17:45:15.727Z
Comments.Text: I kept the three because the goal is not to know the detailed differences between the models .. but just to know if they are all based on "only" fully connected layers and thus they perform the same mathematical operations during inference phase.
Comments.UserDisplayName: witdev
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-04-09T03:17:13.283Z
Comments.Text: I made some changes to your post in an attempt to improve the clarity. Please, check that I didn't change the meaning of the post.
Comments.UserDisplayName: nbro
Tags: <autoencoders><multilayer-perceptrons><dense-layers><deep-belief-network><forward-pass>
Title: What is the difference between the forward pass of the Multi-Layer Perceptron, Deep AutoEncoder and Deep Belief Network?
UserDisplayName: witdev
ThreadText: # What is the difference between the forward pass of the Multi-Layer Perceptron, Deep AutoEncoder and Deep Belief Network?
  
  (Asked by: username_0 on 2021-04-04)
  
  Multi-Layer Perceptron (MLP), Deep AutoEncoder (DAE), and Deep Belief Network (DBN) are trained differently.
  
  However, do they follow the same process during the inference phase, i.e., do they calculate a weighted sum, then apply a non-linear activation function, for each layer until the last layer, or is there any difference? Moreover, are they only composed of fully connected layers?
  
  **Tags:** autoencoders, multilayer-perceptrons, dense-layers, deep-belief-network, forward-pass
  
  > **Comments:**
  > **username_1**: Could you please focus on the comparison of 2 models at a time rather than 3? Moreover, when you use an acronym, like DAE, you should first use the long/full version, like "muli-layer perceptron (MLP)", so that everyone knows what the acronym refers to.  
  > **username_0**: I kept the three because the goal is not to know the detailed differences between the models .. but just to know if they are all based on "only" fully connected layers and thus they perform the same mathematical operations during inference phase.  
  > **username_1**: I made some changes to your post in an attempt to improve the clarity. Please, check that I didn't change the meaning of the post.  
  
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  ## 0 answers
  
  