diff --git a/main.pdf b/main.pdf
index 0883399..5213bfe 100644
--- a/main.pdf
+++ b/main.pdf
@@ -1,3 +1,3 @@
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diff --git a/sections/partie-technique/IA/IA.tex b/sections/partie-technique/IA/IA.tex
index 02cedbe..9888268 100644
--- a/sections/partie-technique/IA/IA.tex
+++ b/sections/partie-technique/IA/IA.tex
@@ -1,6 +1,6 @@
 This section covers our research on artificial intelligence to develop an AI component for the project, particularly for the OCR.
 
-Our first draft of the AI has not been integrated into the main project\footnote{You can find the code source of the project here: https://gitea.louisgallet.fr/lgallet/XOR-NeuralNetwork-C} because it is focused on our research on AI rather than the project itself.
+Our first draft of the AI has not been integrated into the main project\footnote{You can find the code source of the project here: https://gitea.louisgallet.fr/OCRudoku/NXOR-NeuralNetwork-C} because it is focused on our research on AI rather than the project itself.
 
 Below, you will find the research we conducted for the AI.
 
diff --git a/sections/partie-technique/IA/entrainement/entrainement.tex b/sections/partie-technique/IA/entrainement/entrainement.tex
index 4663401..a889016 100644
--- a/sections/partie-technique/IA/entrainement/entrainement.tex
+++ b/sections/partie-technique/IA/entrainement/entrainement.tex
@@ -6,6 +6,6 @@ For each training example, the network performs a forward pass, then applies bac
 
 The output results are displayed at each training step, allowing visualization of the final values of weights and biases.
 \begin{figure}[H]
-	\caption{Example output of the training of the XOR neural network.}
+	\caption{Example output of the training of the NXOR neural network.}
 	\includegraphics[scale=0.5]{sections/partie-technique/IA/entrainement/ia-train-demo.png}
 \end{figure}
diff --git a/sections/partie-technique/IA/entrainement/ia-train-demo.png b/sections/partie-technique/IA/entrainement/ia-train-demo.png
index bac0c6f..13b260b 100644
--- a/sections/partie-technique/IA/entrainement/ia-train-demo.png
+++ b/sections/partie-technique/IA/entrainement/ia-train-demo.png
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diff --git a/sections/partie-technique/IA/structure-neuronne/structure-neuronne.tex b/sections/partie-technique/IA/structure-neuronne/structure-neuronne.tex
index 6a68657..a3b1ac9 100644
--- a/sections/partie-technique/IA/structure-neuronne/structure-neuronne.tex
+++ b/sections/partie-technique/IA/structure-neuronne/structure-neuronne.tex
@@ -1 +1 @@
-The structure of this neural network consists of an input layer, a hidden layer, and an output layer. The network is configured to solve the XOR problem, with binary input values and a binary output. The hidden layer, represented by the array \texttt{hiddenLayer}, is connected to the inputs through weights \texttt{hiddenWeights}, while the output layer, \texttt{outputLayer}, is connected to the hidden layer via the weights \texttt{outputWeights}. The biases for each layer are initialized in \texttt{hiddenLayerBias} and \texttt{outputLayerBias}.
+The structure of this neural network consists of an input layer, a hidden layer, and an output layer. The network is configured to solve the Non-XOR problem, with binary input values and a binary output. The hidden layer, represented by the array \texttt{hiddenLayer}, is connected to the inputs through weights \texttt{hiddenWeights}, while the output layer, \texttt{outputLayer}, is connected to the hidden layer via the weights \texttt{outputWeights}. The biases for each layer are initialized in \texttt{hiddenLayerBias} and \texttt{outputLayerBias}.