perhaps last typo...grrr

doc/pub/NeuralNet/html/._NeuralNet-bs033.html

@@ -390,7 +390,7 @@ $$
 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

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

@@ -369,7 +369,7 @@ 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.

doc/pub/NeuralNet/html/NeuralNet-reveal.html

@@ -1367,7 +1367,7 @@ where the superscript \( l-1 \) indicates that these are the outputs from layer
 Our cost function at the final layer \( l=L \) is now
 <p>&nbsp;<br>
 $$
-\mathcal{C}(\hat{W}) = - \sum_{i=1}^n \left(t_i\log{a_i^L}+(i-t_i)\log{(1-a_i^L)}\right),
+\mathcal{C}(\hat{W}) = - \sum_{i=1}^n \left(t_i\log{a_i^L}+(1-t_i)\log{(1-a_i^L)}\right),
 $$
 <p>&nbsp;<br>
 
@@ -4048,7 +4048,7 @@ $$
 <p>&nbsp;<br>
 
 <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.

doc/pub/NeuralNet/html/NeuralNet-solarized.html

@@ -1362,7 +1362,7 @@ $$
 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
@@ -3909,7 +3909,7 @@ 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.

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.

doc/pub/NeuralNet/ipynb/NeuralNet.ipynb

@@ -1549,7 +1549,7 @@
    "metadata": {},
    "source": [
     "$$\n",
-    "\\mathcal{C}(\\hat{W}) = - \\sum_{i=1}^n \\left(t_i\\log{a_i^L}+(i-t_i)\\log{(1-a_i^L)}\\right),\n",
+    "\\mathcal{C}(\\hat{W}) = - \\sum_{i=1}^n \\left(t_i\\log{a_i^L}+(1-t_i)\\log{(1-a_i^L)}\\right),\n",
     "$$"
    ]
   },
@@ -4352,7 +4352,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In order to minimize it, an optimalization method must be chosen. \n",
+    "In order to minimize it, an optimization method must be chosen. \n",
     "\n",
     "Here, gradient descent with a constant step size has been chosen. \n",
     "\n",

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.