perhaps last typo...grrr

doc/pub/NeuralNet/html/NeuralNet.html

@@ -1367,7 +1367,7 @@ $$
 where the superscript \( l-1 \) indicates that these are the outputs from layer \( l-1 \).
 Our cost function at the final layer \( l=L \) is now
 $$
-\mathcal{C}(\hat{W}) = - \sum_{i=1}^n \left(t_i\log{a_i^L}+(i-t_i)\log{(1-a_i^L)}\right),
+\mathcal{C}(\hat{W}) = - \sum_{i=1}^n \left(t_i\log{a_i^L}+(1-t_i)\log{(1-a_i^L)}\right),
 $$
 
 where we have defined the targets \( t_i \). The derivatives of the cost function with respect to the output \( a_i^L \) are then easily calculated and we get
@@ -3914,7 +3914,7 @@ c(x, P) = \sum_i  \big(g_t'(x_i, P) - ( -\gamma g_t(x_i, P) \big)^2
 $$
 
 <p>
-In order to minimize it, an optimalization method must be chosen.
+In order to minimize it, an optimization method must be chosen.
 
 <p>
 Here, gradient descent with a constant step size has been chosen.