Body: <p>I have a simple AI built out in an Excel sheet for testing. It has 4 inputs, 1 hidden layer with 4 Neurons, and 4 outputs. I am trying to add a second hidden layer, with 4 Neurons. I am using backpropagation. I understand the point where one hidden layer's weights and bias are adjusted based off the error from the output sigmoid vs the actual desired output. But how do I determine that for the hidden layer prior in a 2 hidden layer AI? Does the backpropagation need to adjust the weights and bias in the output layer first?</p>
  <p>I am new to learning AI and want to understand it from the ground up, so please bare with me! I do not fully understand the math symbols involved in all this, my background is in programming, so some simple guidance in non-calculus language would be really helpful. I am also reverse engineering this in SQL. I started with an excel sheet from this site that is only 2 inputs and outputs, and I am expanding on it, but is essentially the same.</p>
  <p><a href="https://www.codeproject.com/Articles/1273000/Machine-Learning-in-Excel" rel="nofollow noreferrer">https://www.codeproject.com/Articles/1273000/Machine-Learning-in-Excel</a></p>
  <p>I have used ChatGPT and tried to read and understand from various sites and have gotten this far but this is really stumping me.</p>
  <p>Thanks!</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-11-15T11:59:17.463Z
Comments.Text: I don't understand this question "But how do I determine that for the hidden layer prior in a 2 hidden layer AI?" Are you asking how do you determine the weights for this new layer you want to add? The general answer is: backpropagation. But I suppose you're asking for the formulas and an explanation of how back-propagation would work for a 2-hidden layer NN, right?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-11-18T03:59:25.417Z
Comments.Text: There is a video on backpropagation by a Stanford grad who posts about math under the name 3blue1brown. I recommend it. His videos have a lot of helpful visualizations to build intuition around the math.
Comments.UserDisplayName: Jim McGaw
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-11-18T05:19:23.760Z
Comments.Text: @nbro Yes thats correct. I understand how it works in a 1 hidden layer network, the idea being you are taking the difference between the output vs desired, and doing some maths and applying it backwards. But in a multi layer network, does this mean I take some output vs desired in between HL 1 and HL 2? Do I run backpropagation on HL 2, then somehow look at what HL 1 is outputting vs a desired in order to activate HL 2 properly? Just confused on this part.
Comments.UserDisplayName: Xenophage
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-11-18T05:19:54.707Z
Comments.Text: @JimMcGaw thank you I will definitely be taking a look at this! Appreciate it!
Comments.UserDisplayName: Xenophage
Tags: <deep-learning><hidden-layers><mean-squared-error>
Title: How do I find the actual vs difference for calculating backpropagation on the first hidden layer in a AI with 2 hidden layers?
UserDisplayName: Xenophage
ThreadText: # How do I find the actual vs difference for calculating backpropagation on the first hidden layer in a AI with 2 hidden layers?
  
  (Asked by: username_0 on 2024-11-13)
  
  I have a simple AI built out in an Excel sheet for testing. It has 4 inputs, 1 hidden layer with 4 Neurons, and 4 outputs. I am trying to add a second hidden layer, with 4 Neurons. I am using backpropagation. I understand the point where one hidden layer's weights and bias are adjusted based off the error from the output sigmoid vs the actual desired output. But how do I determine that for the hidden layer prior in a 2 hidden layer AI? Does the backpropagation need to adjust the weights and bias in the output layer first?
  
  I am new to learning AI and want to understand it from the ground up, so please bare with me! I do not fully understand the math symbols involved in all this, my background is in programming, so some simple guidance in non-calculus language would be really helpful. I am also reverse engineering this in SQL. I started with an excel sheet from this site that is only 2 inputs and outputs, and I am expanding on it, but is essentially the same.
  
  <https://www.codeproject.com/Articles/1273000/Machine-Learning-in-Excel>
  
  I have used ChatGPT and tried to read and understand from various sites and have gotten this far but this is really stumping me.
  
  Thanks!
  
  **Tags:** deep-learning, hidden-layers, mean-squared-error
  
  > **Comments:**
  > **username_1**: I don't understand this question "But how do I determine that for the hidden layer prior in a 2 hidden layer AI?" Are you asking how do you determine the weights for this new layer you want to add? The general answer is: backpropagation. But I suppose you're asking for the formulas and an explanation of how back-propagation would work for a 2-hidden layer NN, right?  
  > **username_2**: There is a video on backpropagation by a Stanford grad who posts about math under the name 3blue1brown. I recommend it. His videos have a lot of helpful visualizations to build intuition around the math.  
  > **username_0**: @username_1 Yes thats correct. I understand how it works in a 1 hidden layer network, the idea being you are taking the difference between the output vs desired, and doing some maths and applying it backwards. But in a multi layer network, does this mean I take some output vs desired in between HL 1 and HL 2? Do I run backpropagation on HL 2, then somehow look at what HL 1 is outputting vs a desired in order to activate HL 2 properly? Just confused on this part.  
  > **username_0**: @username_2 thank you I will definitely be taking a look at this! Appreciate it!  
  
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