<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>

CC BY-SA 4.0
2020-08-17T07:20:07.450Z
2020-08-17T07:20:07.450Z
Neil Slater
<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>

<neural-networks><recurrent-neural-networks><backpropagation>
Can the normal equation be used to optimise the RNN's weights?
jr123456jr987654321
# 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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