additional typos

doc/pub/NeuralNet/html/._NeuralNet-bs099.html

@@ -370,24 +370,24 @@ The idea of the gradient descent algorithm is to update parameters in
 direction where the cost function decreases goes to a minimum.
 
 <p>
-In general, the update of some parameters \( \vec \omega \) given a cost
-function defined by some weights \( \vec \omega \), \( c(x, \vec \omega) \),
+In general, the update of some parameters \( \hat{\omega} \) given a cost
+function defined by some weights \( \hat{\omega} \), \( c(x, \hat{\omega}) \),
 goes as follows:
 
 $$
-\vec \omega_{\mathrm{new} } = \vec \omega - \lambda \nabla_{\vec \omega} c(x, \vec \omega),
+\hat{\omega}_{\mathrm{new} } = \hat{\omega} - \lambda \nabla_{\hat{\omega}} c(x, \hat{\omega}),
 $$
 
 <p>
-for a number of iterations or until $ \big|\big| \vec
-\omega_{\mathrm{new} } - \vec \omega \big|\big|$ is smaller than some
+for a number of iterations or until \( \big|\big| \hat{\omega}_{\mathrm{new} } - \hat{\omega} \big|\big| \) 
+is smaller than some
 given tolerance.
 
 <p>
 The value of \( \lambda \) decides how large steps the algorithm must take
-in the direction of $ \nabla_{\vec \omega} c(x, \vec \omega)$. The
-notatation \( \nabla_{\vec \omega} \) denotes the gradient with respect to
-the elements in \( \vec \omega \).
+in the direction of $ \nabla_{\hat{\omega}} c(x, \hat{\omega})$. The
+notatation \( \nabla_{\hat{\omega}} \) denotes the gradient with respect to
+the elements in \( \hat{\omega} \).
 
 <p>
 <p>