Refactor code formatting and improve readability in Jupyter notebooks for TP_4 and TP_5

- Adjusted indentation and line breaks for better clarity in function definitions and import statements.
- Standardized string quotes for consistency across the codebase.
- Enhanced readability of DataFrame creation and manipulation by breaking long lines into multiple lines.
- Cleaned up print statements and comments for improved understanding.
- Ensured consistent use of whitespace around operators and after commas.

M2/Deep Learning/TP2 - Convolution/TP2 - Bonus Starter _ ResNet.ipynb

@@ -24,20 +24,30 @@
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "\n",
-    "sns.set(style='whitegrid')\n",
+    "sns.set(style=\"whitegrid\")\n",
     "\n",
     "import tensorflow as tf\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "from tensorflow import keras\n",
     "\n",
-    "(X_train_full, y_train_full), (X_test, y_test) = (keras.datasets.fashion_mnist.load_data())\n",
-    "X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, train_size=0.8)\n",
+    "(X_train_full, y_train_full), (X_test, y_test) = (\n",
+    "    keras.datasets.fashion_mnist.load_data()\n",
+    ")\n",
+    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
+    "    X_train_full, y_train_full, train_size=0.8\n",
+    ")\n",
     "\n",
     "scaler = StandardScaler()\n",
-    "X_train = scaler.fit_transform(X_train.astype(np.float32).reshape(-1, 28 * 28)).reshape(-1, 28, 28, 1)\n",
-    "X_valid = scaler.transform(X_valid.astype(np.float32).reshape(-1, 28 * 28)).reshape(-1, 28, 28, 1)\n",
-    "X_test = scaler.transform(X_test.astype(np.float32).reshape(-1, 28 * 28)).reshape(-1, 28, 28, 1)"
+    "X_train = scaler.fit_transform(X_train.astype(np.float32).reshape(-1, 28 * 28)).reshape(\n",
+    "    -1, 28, 28, 1\n",
+    ")\n",
+    "X_valid = scaler.transform(X_valid.astype(np.float32).reshape(-1, 28 * 28)).reshape(\n",
+    "    -1, 28, 28, 1\n",
+    ")\n",
+    "X_test = scaler.transform(X_test.astype(np.float32).reshape(-1, 28 * 28)).reshape(\n",
+    "    -1, 28, 28, 1\n",
+    ")"
    ]
   },
   {

M2/Deep Learning/TP2 - Convolution/TP2 - Starter.ipynb

@@ -26,11 +26,13 @@
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "\n",
-    "sns.set(style='whitegrid')\n",
+    "sns.set(style=\"whitegrid\")\n",
     "\n",
     "from tensorflow import keras\n",
     "\n",
-    "(X_train_full, y_train_full), (X_test, y_test) = (keras.datasets.fashion_mnist.load_data())"
+    "(X_train_full, y_train_full), (X_test, y_test) = (\n",
+    "    keras.datasets.fashion_mnist.load_data()\n",
+    ")"
    ]
   },
   {
@@ -186,7 +188,7 @@
     "        keras.layers.Dense(units=64, activation=\"relu\"),\n",
     "        keras.layers.Dense(units=10, activation=\"softmax\"),\n",
     "    ]\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -627,10 +629,7 @@
     "            batch_size=batch_size,\n",
     "            validation_data=(X_valid, y_valid),\n",
     "        )\n",
-    "        training_curves.append({\n",
-    "            'history': history,\n",
-    "            'normalization': normalized\n",
-    "        })"
+    "        training_curves.append({\"history\": history, \"normalization\": normalized})"
    ]
   },
   {
@@ -653,7 +652,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def agregate_result(results: list, normalized: bool, metric_name: str = 'accuracy') -> pd.DataFrame:\n",
+    "def agregate_result(\n",
+    "    results: list, normalized: bool, metric_name: str = \"accuracy\"\n",
+    ") -> pd.DataFrame:\n",
     "    train_curves = []\n",
     "    val_curves = []\n",
     "\n",
@@ -663,7 +664,7 @@
     "            train_curves.append(hist_obj.history[metric_name])\n",
     "            val_curves.append(hist_obj.history[f\"val_{metric_name}\"])\n",
     "\n",
-    "    return np.array(train_curves).flatten(), np.array(val_curves).flatten()\n"
+    "    return np.array(train_curves).flatten(), np.array(val_curves).flatten()"
    ]
   },
   {
@@ -697,7 +698,9 @@
     "for idx, metric in enumerate(metrics):\n",
     "    ax = axs[idx]\n",
     "    for normalized in [True, False]:\n",
-    "        train, val = agregate_result(training_curves, normalized=normalized, metric_name=metric)\n",
+    "        train, val = agregate_result(\n",
+    "            training_curves, normalized=normalized, metric_name=metric\n",
+    "        )\n",
     "        train_runs = train.reshape(-1, epochs)\n",
     "        val_runs = val.reshape(-1, epochs)\n",
     "\n",
@@ -710,10 +713,22 @@
     "        label_prefix = \"With BN\" if normalized else \"Without BN\"\n",
     "\n",
     "        ax.plot(mean_train, label=label_prefix, color=color, linestyle=\"-\")\n",
-    "        ax.fill_between(range(epochs), mean_train - std_train, mean_train + std_train, color=color, alpha=0.2)\n",
+    "        ax.fill_between(\n",
+    "            range(epochs),\n",
+    "            mean_train - std_train,\n",
+    "            mean_train + std_train,\n",
+    "            color=color,\n",
+    "            alpha=0.2,\n",
+    "        )\n",
     "\n",
     "        ax.plot(mean_val, color=color, linestyle=\"--\")\n",
-    "        ax.fill_between(range(epochs), mean_val - std_val, mean_val + std_val, color=color, alpha=0.2)\n",
+    "        ax.fill_between(\n",
+    "            range(epochs),\n",
+    "            mean_val - std_val,\n",
+    "            mean_val + std_val,\n",
+    "            color=color,\n",
+    "            alpha=0.2,\n",
+    "        )\n",
     "\n",
     "    ax.set_title(f\"Training and Validation {metric.capitalize()}\")\n",
     "    ax.set_xlabel(\"Epochs\")\n",
@@ -721,7 +736,7 @@
     "    ax.legend()\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {