Answers.Body: <p>Your understanding appears correct. One formula I want to nitpick about though, is that of the delta calculation for the hidden layer. I would suggest framing it as :</p>
  <pre><code>SUM of (delta * connection weight ) * derivative
  </code></pre>
  <p>rather than :</p>
  <pre><code>(SUM of delta * connection weight ) * derivative
  </code></pre>
  
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Answers.CreationDate: 2023-10-18T23:25:01.793Z
Answers.Text: I will edit the question to improve the question according to your suggestion. Thanks!
Answers.UserDisplayName: will The J
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Body: <p>I'm trying to study how backpropagation works step by step in a MultiLayer Perceptron neural network.  I would really like to be able to understand how these calculations work.  And I have a specific question I would like to ask.  The formulas <strong>I'm trying to learn are the following to calculate the delta, and then to update the weights and bias:</strong></p>
  <p><strong>Please, could you help me understand if is correct ?</strong></p>
  <p><strong>In Output Layer</strong></p>
  <pre><code>Calculate delta: error * derivative 
  
  Update weights: newW = oldW + (delta * neuron entry * learning_rate) 
  
  Update Bias: newB = oldB + (delta * learning_rate)
  </code></pre>
  <p>(Here in the output layer, the neuron's error is the difference between the desired output and the predicted output of the neuron, in the feedforward phase)</p>
  <p><em><strong>In the Hidden Layer:</strong></em></p>
  <pre><code>Calculate delta: SUM of (delta * connection weight ) * derivative
  
   Update weights: newW = oldW + (delta * neuron entry * learning_rate)
  
   Update Bias: newB = oldB + (delta * learning_rate)
  </code></pre>
  <p>(In the SUM, Here, in the hidden layer, the error of a neuron is a summation, adding the delta multiplied by the connection weight of the neuron in this next layer with the neuron in the current layer, of each neuron in the next layer, that is, the connection weights that connect a neuron in the current hidden layer with a neuron in the next layer.)</p>
  <p><strong>I would like to ask you a specific question about these two formulas used in backpropagation, because I have this doubt: the formulas I am studying to calculate the delta and then to update the weights in the output layer and hidden layer are correct, Are these formulas I mentioned correct?</strong></p>
  <p><em>Please, could someone help me, and tell me if what I am studying is correct?</em></p>
  <p>What are the backpropagation correct formulas?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-10-14T16:10:24.643Z
Comments.Text: I'm sorry, in the end I cannot make out what you are asking.  I do not know what "It is like this the same?" means.
Comments.UserDisplayName: David Hoelzer
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-10-14T18:36:58.477Z
Comments.Text: Sorry in the end. Thank you for wanting to help. With that, what I would like to ask is if the formulas are correct?
Comments.UserDisplayName: will The J
Tags: <backpropagation>
Title: what is the backpropagation formula to calculate delta and update weights?
UserDisplayName: will The J
ThreadText: # what is the backpropagation formula to calculate delta and update weights?
  
  (Asked by: username_0 on 2023-10-14)
  
  I'm trying to study how backpropagation works step by step in a MultiLayer Perceptron neural network.  I would really like to be able to understand how these calculations work.  And I have a specific question I would like to ask.  The formulas I'm trying to learn are the following to calculate the delta, and then to update the weights and bias:
  Please, could you help me understand if is correct ?
  In Output Layer
  Calculate delta: error * derivative 
  
  Update weights: newW = oldW + (delta * neuron entry * learning_rate) 
  
  Update Bias: newB = oldB + (delta * learning_rate)
  
  (Here in the output layer, the neuron's error is the difference between the desired output and the predicted output of the neuron, in the feedforward phase)
  In the Hidden Layer:
  Calculate delta: SUM of (delta * connection weight ) * derivative
  
   Update weights: newW = oldW + (delta * neuron entry * learning_rate)
  
   Update Bias: newB = oldB + (delta * learning_rate)
  
  (In the SUM, Here, in the hidden layer, the error of a neuron is a summation, adding the delta multiplied by the connection weight of the neuron in this next layer with the neuron in the current layer, of each neuron in the next layer, that is, the connection weights that connect a neuron in the current hidden layer with a neuron in the next layer.)
  I would like to ask you a specific question about these two formulas used in backpropagation, because I have this doubt: the formulas I am studying to calculate the delta and then to update the weights in the output layer and hidden layer are correct, Are these formulas I mentioned correct?
  Please, could someone help me, and tell me if what I am studying is correct?
  What are the backpropagation correct formulas?
  
  **Tags:** backpropagation
  > **Comments:**
  > **username_1**: I'm sorry, in the end I cannot make out what you are asking.  I do not know what "It is like this the same?" means.  
  > **username_0**: Sorry in the end. Thank you for wanting to help. With that, what I would like to ask is if the formulas are correct?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2023-10-14. Score: 1 | Accepted answer
  
  Your understanding appears correct. One formula I want to nitpick about though, is that of the delta calculation for the hidden layer. I would suggest framing it as :
  SUM of (delta * connection weight ) * derivative
  
  rather than :
  (SUM of delta * connection weight ) * derivative
  
  > **Comments:**
  > **username_0**: I will edit the question to improve the question according to your suggestion. Thanks!  
  
  ---
  
  