perhaps last typo...grrr

doc/src/NeuralNet/NeuralNet.do.txt

@@ -995,7 +995,7 @@ 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
 !bt
 \[
-\mathcal{C}(\hat{W}) = - \sum_{i=1}^n \left(t_i\log{a_i^L}+(i-t_i)\log{(1-a_i^L)}\right),
+\mathcal{C}(\hat{W}) = - \sum_{i=1}^n \left(t_i\log{a_i^L}+(1-t_i)\log{(1-a_i^L)}\right),
 \]
 !et
 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
@@ -3281,7 +3281,7 @@ c(x, P) = \sum_i  \big(g_t'(x_i, P) - ( -\gamma g_t(x_i, P) \big)^2
 \]
 !et
 
-In order to minimize it, an optimalization method must be chosen. 
+In order to minimize it, an optimization method must be chosen. 
 
 Here, gradient descent with a constant step size has been chosen.