feat: ✨ Finish IA part

sections/partie-technique/IA/fonction-activation/fonction-activation.tex

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+The activation function used for each neuron in the hidden and output layers is the \textit{sigmoid}, defined by the \texttt{sigmoid} function:
+\begin{verbatim}
+double sigmoid(double x) {
+  if (x > 20) return 1.0;
+  if (x < -20) return 0.0;
+  double z = exp(-x);
+  return 1.0 / (1.0 + z);
+}
+\end{verbatim}
+This function is bounded between 0 and 1, allowing for normalization of the activation values for each neuron. The derivative of the sigmoid, \texttt{sigmoid\_derivative}, is used in backpropagation to compute gradients:
+\begin{verbatim}
+double sigmoid_derivative(double x) {
+  return x * (1.0 - x);
+}
+\end{verbatim}