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 Optimisation/ComputerSession2.ipynb

@@ -308,7 +308,6 @@
     }
    ],
    "source": [
-    "import numpy as np\n",
     "\n",
     "u = lambda x: np.sqrt((6 - x) ** 2 + 4)\n",
     "\n",
@@ -364,7 +363,9 @@
     "# Run Newton's method\n",
     "optimal_point_newton, iterations_newton = newton_method(initial_guess_newton)\n",
     "print(f\"Optimal point (Newton): {optimal_point_newton}\")\n",
-    "print(f\"Objective function value at optimal point (Newton): {objective_function(optimal_point_newton)}\")\n",
+    "print(\n",
+    "    f\"Objective function value at optimal point (Newton): {objective_function(optimal_point_newton)}\"\n",
+    ")\n",
     "print(f\"Number of iterations (Newton): {iterations_newton}\")\n",
     "\n",
     "# Initial interval for dichotomy method\n",
@@ -373,7 +374,9 @@
     "# Run dichotomy method\n",
     "optimal_point_dichotomy, iterations_dichotomy = dichotomy_method(aL, aR)\n",
     "print(f\"Optimal point (Dichotomy): {optimal_point_dichotomy}\")\n",
-    "print(f\"Objective function value at optimal point (Dichotomy): {objective_function(optimal_point_dichotomy)}\")\n",
+    "print(\n",
+    "    f\"Objective function value at optimal point (Dichotomy): {objective_function(optimal_point_dichotomy)}\"\n",
+    ")\n",
     "print(f\"Number of iterations (Dichotomy): {iterations_dichotomy}\")"
    ]
   },
@@ -564,9 +567,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "\n"
-   ]
+   "source": []
   }
  ],
  "metadata": {