Refactor code for improved readability and consistency across multiple Jupyter notebooks

- Added missing commas in various print statements and function calls for better syntax.
- Reformatted code to enhance clarity, including breaking long lines and aligning parameters.
- Updated function signatures to use float type for sigma parameters instead of int for better precision.
- Cleaned up comments and documentation strings for clarity and consistency.
- Ensured consistent formatting in plotting functions and data handling.

M2/Deep Learning/TP4 - Récurrents/TP4 - Bonus.ipynb

@@ -17,10 +17,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import keras\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
     "\n",
+    "import keras\n",
+    "\n",
     "sns.set(style=\"whitegrid\")\n",
     "\n",
     "\n",
@@ -149,7 +150,7 @@
     "        keras.layers.Embedding(\n",
     "            input_dim=vocabulary_size,\n",
     "            output_dim=dimension,\n",
-    "        )\n",
+    "        ),\n",
     "    )\n",
     "    model.add(keras.layers.SimpleRNN(128, return_sequences=False))\n",
     "    model.add(keras.layers.Dense(vocabulary_size, activation=\"softmax\"))\n",