Answers.Body: <p>You could try to use it as an exponent, and it will still learn... However, you will almost certainly end up with numeric instability very quickly with values either exploding or vanishing.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-09-13T17:45:08.993Z
Answers.LastActivityDate: 2022-09-13T17:45:08.993Z
Answers.UserDisplayName: David Hoelzer
Body: <p>So why weight should be multiplied with input?</p>
  <p>Yes I know, weight is intended for tuning the connection strength of input that will affect output so that's will be useful for learning (CMIIW).</p>
  <p>But why the input should be MULTIPLIED by weight? Why doesn't use EXPONENT operation?</p>
  <p>like this,</p>
  <pre><code>y_hat = x_1^w_1 + x_2^w_2 + bias
  </code></pre>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-09-14T00:18:45.627Z
Comments.Text: A similar question is asked here: https://www.quora.com/When-calculating-weights-in-a-neural-network-why-do-you-multiply-the-error-signal-by-the-input  it's not always the case that multiplcation is used, but using exponential would case too large a number very quickly, see: https://towardsdatascience.com/everything-you-need-to-know-about-activation-functions-in-deep-learning-models-84ba9f82c253
Comments.UserDisplayName: Rob
Tags: <neural-networks><weights><artificial-neuron>
Title: Why the x-input should be multiplied by weight in an artificial neuron?
UserDisplayName: Muhammad Ikhwan Perwira
ThreadText: # Why the x-input should be multiplied by weight in an artificial neuron?
  
  (Asked by: username_0 on 2022-09-13)
  
  So why weight should be multiplied with input?
  
  Yes I know, weight is intended for tuning the connection strength of input that will affect output so that's will be useful for learning (CMIIW).
  
  But why the input should be MULTIPLIED by weight? Why doesn't use EXPONENT operation?
  
  like this,
  
  ```
  y_hat = x_1^w_1 + x_2^w_2 + bias
  
  ```
  
  **Tags:** neural-networks, weights, artificial-neuron
  
  > **Comments:**
  > **username_1**: A similar question is asked here: https://www.quora.com/When-calculating-weights-in-a-neural-network-why-do-you-multiply-the-error-signal-by-the-input  it's not always the case that multiplcation is used, but using exponential would case too large a number very quickly, see: https://towardsdatascience.com/everything-you-need-to-know-about-activation-functions-in-deep-learning-models-84ba9f82c253  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2022-09-13. Score: 2 | Accepted answer
  
  You could try to use it as an exponent, and it will still learn... However, you will almost certainly end up with numeric instability very quickly with values either exploding or vanishing.
  
  
  ---
  
  