diff --git a/M1/Numerical Methods/TP2.ipynb b/M1/Numerical Methods/TP2.ipynb
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+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# Automatic Differentiation\n",
+    "\n",
+    "### Neural Network\n",
+    "\n",
+    "Loss function: softmax layer in $\\mathbb{R}^3$\n",
+    "\n",
+    "Architecture: FC/ReLU 4-5-7-3"
+   ],
+   "id": "c897654e0a140cbd"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-03-24T13:10:01.794956Z",
+     "start_time": "2025-03-24T13:09:58.777694Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "import numpy as np\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.datasets import make_classification\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "accuracies = []\n",
+    "\n",
+    "for _ in range(10):\n",
+    "    X, y = make_classification(n_samples=1000, n_features=4, n_classes=3, n_clusters_per_class=1)\n",
+    "\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
+    "    model = MLPClassifier(hidden_layer_sizes=(5, 7), activation='relu', max_iter=10000, solver='adam')\n",
+    "    model.fit(X_train, y_train)\n",
+    "\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    accuracies.append(accuracy_score(y_test, y_pred))\n",
+    "\n",
+    "print(f'Accuracy: {np.mean(accuracies) * 100:.0f}%')\n",
+    "print(f\"Max accuracy: {np.max(accuracies) * 100:.0f}%\")\n",
+    "print(f\"Min accuracy: {np.min(accuracies) * 100:.0f}%\")"
+   ],
+   "id": "70a4eb1d928b10d0",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 93%\n",
+      "Max accuracy: 100%\n",
+      "Min accuracy: 80%\n"
+     ]
+    }
+   ],
+   "execution_count": 21
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "96b6d46883ed5570"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}