Answers.Body: <p><a href="https://i.sstatic.net/cWtmKIlg.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/cWtmKIlg.png" alt="enter image description here" /></a></p>
  <p>This is the RNN architecture. Image is taken from <a href="https://en.wikipedia.org/wiki/Recurrent_neural_network" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Recurrent_neural_network</a>.</p>
  <p>Here <span class="math-container">$U = B$</span>, <span class="math-container">$V = A$</span>, and <span class="math-container">$W = C$</span>.</p>
  <p>We are using ReLU as the activation function. <span class="math-container">$\phi(x) = max(0, x)$</span></p>
  <p><strong>Computing <span class="math-container">$y_1$</span></strong></p>
  <p><span class="math-container">$x_1 = 3$</span>, <span class="math-container">$h_1$</span> = <span class="math-container">$\phi(U.x_1 + V.h_0)$</span>= <span class="math-container">$\phi \left ( \begin{bmatrix}1\\2\end{bmatrix} *3 + \begin{bmatrix}1&amp;-1\\1&amp;1\end{bmatrix}\begin{bmatrix}0 \\ 0\end{bmatrix} \right)=\begin{bmatrix}3 \\ 6\end{bmatrix}$</span></p>
  <p><span class="math-container">$y_1 = V.h_1 = \begin{bmatrix}-1 \ 1\end{bmatrix}* \begin{bmatrix}3\\6\end{bmatrix} = 3$</span></p>
  <p><strong>Computing <span class="math-container">$y_2$</span></strong></p>
  <p><span class="math-container">$x_2 = 4$</span>, <span class="math-container">$h_2$</span> = <span class="math-container">$\phi(U.x_2 + V.h_1)$</span>= <span class="math-container">$\phi \left ( \begin{bmatrix}1\\2\end{bmatrix} *4 + \begin{bmatrix}1&amp;-1\\1&amp;1\end{bmatrix}\begin{bmatrix}3 \\ 6\end{bmatrix} \right)=\begin{bmatrix}1 \\ 17\end{bmatrix}$</span></p>
  <p><span class="math-container">$y_2 = V.h_2 = \begin{bmatrix}-1 \ 1\end{bmatrix}* \begin{bmatrix}1\\17\end{bmatrix} = 16$</span></p>
  <p>You can compute <span class="math-container">$y_3$</span> and <span class="math-container">$y_4$</span> similarly.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-05-17T13:56:42.843Z
Answers.LastActivityDate: 2024-05-17T14:01:18.677Z
Answers.LastEditDate: 2024-05-17T14:01:18.677Z
Answers.UserDisplayName: vishal
Body: <p><a href="https://i.sstatic.net/0b2rpJCY.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/0b2rpJCY.png" alt="Recurrent Neural Network Calculation by hand by Tom Yeh" /></a></p>
  <p>As you can see, this is a recurrent neural network. I want to understand how the calculations are being made. Please, be as detailed as possible no matter how simple or self-explanatory the calculations are. It would be much better if you can show the calculations on paper and upload its image. Also, please don't mind if the question seems too easy. I have to understand this, as it's part of my course work. I tried doing the calculations but I can't figure it out.</p>
  <p>This image is taken from: <a href="https://www.linkedin.com/posts/tom-yeh_deeplearning-neuralnetworks-generativeai-activity-7142506286788620288-jLN4/" rel="nofollow noreferrer">https://www.linkedin.com/posts/tom-yeh_deeplearning-neuralnetworks-generativeai-activity-7142506286788620288-jLN4/</a></p>
  
Tags: <neural-networks><deep-learning><recurrent-neural-networks>
Title: Can anyone please explain the Recurrent Neural Network calculation shown in the picture?
UserDisplayName: Syed_Hamza_Akbar_Ali
ThreadText: # Can anyone please explain the Recurrent Neural Network calculation shown in the picture?
  
  (Asked by: username_0 on 2024-05-16)
  
  [![Recurrent Neural Network Calculation by hand by Tom Yeh](https://i.sstatic.net/0b2rpJCY.png)](https://i.sstatic.net/0b2rpJCY.png)
  
  As you can see, this is a recurrent neural network. I want to understand how the calculations are being made. Please, be as detailed as possible no matter how simple or self-explanatory the calculations are. It would be much better if you can show the calculations on paper and upload its image. Also, please don't mind if the question seems too easy. I have to understand this, as it's part of my course work. I tried doing the calculations but I can't figure it out.
  
  This image is taken from: <https://www.linkedin.com/posts/tom-yeh_deeplearning-neuralnetworks-generativeai-activity-7142506286788620288-jLN4/>
  
  **Tags:** neural-networks, deep-learning, recurrent-neural-networks
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-05-17. Score: 1
  
  [![enter image description here](https://i.sstatic.net/cWtmKIlg.png)](https://i.sstatic.net/cWtmKIlg.png)
  
  This is the RNN architecture. Image is taken from <https://en.wikipedia.org/wiki/Recurrent_neural_network>.
  
  Here $U = B$, $V = A$, and $W = C$.
  
  We are using ReLU as the activation function. $\phi(x) = max(0, x)$
  
  **Computing $y\_1$**
  
  $x\_1 = 3$, $h\_1$ = $\phi(U.x\_1 + V.h\_0)$= $\phi \left ( \begin{bmatrix}1\\2\end{bmatrix} \*3 + \begin{bmatrix}1&-1\\1&1\end{bmatrix}\begin{bmatrix}0 \\ 0\end{bmatrix} \right)=\begin{bmatrix}3 \\ 6\end{bmatrix}$
  
  $y\_1 = V.h\_1 = \begin{bmatrix}-1 \ 1\end{bmatrix}\* \begin{bmatrix}3\\6\end{bmatrix} = 3$
  
  **Computing $y\_2$**
  
  $x\_2 = 4$, $h\_2$ = $\phi(U.x\_2 + V.h\_1)$= $\phi \left ( \begin{bmatrix}1\\2\end{bmatrix} \*4 + \begin{bmatrix}1&-1\\1&1\end{bmatrix}\begin{bmatrix}3 \\ 6\end{bmatrix} \right)=\begin{bmatrix}1 \\ 17\end{bmatrix}$
  
  $y\_2 = V.h\_2 = \begin{bmatrix}-1 \ 1\end{bmatrix}\* \begin{bmatrix}1\\17\end{bmatrix} = 16$
  
  You can compute $y\_3$ and $y\_4$ similarly.
  
  
  ---
  
  