more boring typos

doc/pub/NeuralNet/html/._NeuralNet-bs095.html

@@ -369,28 +369,28 @@ MathJax.Hub.Config({
 The result after weighting the input at the \( i \)-th hidden neuron can be written as a vector:
 $$
 \begin{aligned}
-\vec{z}_{i}^{\text{hidden}} &= \Big( b_i^{\text{hidden}} + w_i^{\text{hidden}}x_1 , \ b_i^{\text{hidden}} + w_i^{\text{hidden}} x_2, \ \dots \, , \ b_i^{\text{hidden}} + w_i^{\text{hidden}} x_N\Big)  \\
+\hat{z}_{i}^{\mathrm{hidden}} &= \Big( b_i^{\mathrm{hidden}} + w_i^{\mathrm{hidden}}x_1 , \ b_i^{\mathrm{hidden}} + w_i^{\mathrm{hidden}} x_2, \ \dots \, , \ b_i^{\mathrm{hidden}} + w_i^{\mathrm{hidden}} x_N\Big)  \\
 &= 
 \begin{pmatrix}
- b_i^{\text{hidden}}  & w_i^{\text{hidden}}
+ b_i^{\mathrm{hidden}}  & w_i^{\mathrm{hidden}}
 \end{pmatrix}
 \begin{pmatrix}
 1  & 1 & \dots & 1 \\
 x_1 & x_2 & \dots & x_N
 \end{pmatrix} \\
-&= \vec{p}_{i, \text{hidden}}^T X
+&= \hat{p}_{i, \mathrm{hidden}}^T X
 \end{aligned}
 $$
 
 <p>
-It is the vector \( \vec{p}_{i, \text{hidden}}^T \) that defines each row
-in \( P_{\text{hidden} } \), which contains the weights for the neural
+It is the vector \( \hat{p}_{i, \mathrm{hidden}}^T \) that defines each row
+in \( P_{\mathrm{hidden} } \), which contains the weights for the neural
 network to minimize according to <a href="._NeuralNet-bs092.html#mjx-eqn-19">(19)</a>.
 
 <p>
-After having found \( \vec{z}_{i}^{\text{hidden}}  \) for every neuron \( i \)
+After having found \( \hat{z}_{i}^{\mathrm{hidden}}  \) for every neuron \( i \)
 in the hidden layer, the vector will be sent to an activation function
-\( a_i(\vec{z}) \).  In this example, the sigmoid function has been used:
+\( a_i(\hat{z}) \).  In this example, the sigmoid function has been used:
 
 $$
 f(z) = \frac{1}{1 + \exp{(-z)}}.