Answers.Body: <p>Unfortunately, this is not possible. The normal equation can only directly optimise a single layer that connects input and output. There is no equivalent for multiple layers such as those in any neural network architecture.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-08-17T07:20:07.450Z
Answers.LastActivityDate: 2020-08-17T07:20:07.450Z
Answers.UserDisplayName: Neil Slater
Body: <p>I have made an RNN from scratch in Tensorflow.js. In order to update my weights (without needing to calculate the derivatives), I thought of using the normal equation to find the optimal values for my RNN's weights. Would you recommend this approach and if not why?</p>
  
Tags: <neural-networks><recurrent-neural-networks><backpropagation>
Title: Can the normal equation be used to optimise the RNN's weights?
UserDisplayName: jr123456jr987654321
ThreadText: # Can the normal equation be used to optimise the RNN's weights?
  
  (Asked by: username_0 on 2020-08-17)
  
  I have made an RNN from scratch in Tensorflow.js. In order to update my weights (without needing to calculate the derivatives), I thought of using the normal equation to find the optimal values for my RNN's weights. Would you recommend this approach and if not why?
  
  **Tags:** neural-networks, recurrent-neural-networks, backpropagation
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2020-08-17. Score: 2 | Accepted answer
  
  Unfortunately, this is not possible. The normal equation can only directly optimise a single layer that connects input and output. There is no equivalent for multiple layers such as those in any neural network architecture.
  
  
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