Refactor code for improved readability and consistency across notebooks

- Standardized spacing around operators and function arguments in TP7_Kmeans.ipynb and neural_network.ipynb.
- Enhanced the formatting of model building and training code in neural_network.ipynb for better clarity.
- Updated the pyproject.toml to remove a specific TensorFlow version and added linting configuration for Ruff.
- Improved comments and organization in the code to facilitate easier understanding and maintenance.

M1/Numerical Methods/TP2.ipynb

@@ -1,8 +1,9 @@
 {
  "cells": [
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "c897654e0a140cbd",
+   "metadata": {},
    "source": [
     "# Automatic Differentiation\n",
     "\n",
@@ -11,42 +12,18 @@
     "Loss function: softmax layer in $\\mathbb{R}^3$\n",
     "\n",
     "Architecture: FC/ReLU 4-5-7-3"
-   ],
-   "id": "c897654e0a140cbd"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "70a4eb1d928b10d0",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-03-24T15:16:27.015669Z",
      "start_time": "2025-03-24T15:16:23.856887Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "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'Mean Accuracy: {np.mean(accuracies) * 100:.0f}%')\n",
-    "print(f'STD Accuracy: {np.std(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",
@@ -59,20 +36,47 @@
      ]
     }
    ],
-   "execution_count": 33
+   "source": [
+    "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",
+    "        n_samples=1000, n_features=4, n_classes=3, n_clusters_per_class=1\n",
+    "    )\n",
+    "\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
+    "    model = MLPClassifier(\n",
+    "        hidden_layer_sizes=(5, 7), activation=\"relu\", max_iter=10000, solver=\"adam\"\n",
+    "    )\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\"Mean Accuracy: {np.mean(accuracies) * 100:.0f}%\")\n",
+    "print(f\"STD Accuracy: {np.std(accuracies) * 100:.0f}%\")\n",
+    "print(f\"Max accuracy: {np.max(accuracies) * 100:.0f}%\")\n",
+    "print(f\"Min accuracy: {np.min(accuracies) * 100:.0f}%\")"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96b6d46883ed5570",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-03-24T14:37:53.507776Z",
      "start_time": "2025-03-24T14:37:53.505376Z"
     }
    },
-   "cell_type": "code",
-   "source": "",
-   "id": "96b6d46883ed5570",
    "outputs": [],
-   "execution_count": null
+   "source": []
   }
  ],
  "metadata": {