diff --git a/lectures/notebooks/3_gaussians.txt b/lectures/notebooks/3_gaussians.txt
index 57e067d..2403c99 100644
--- a/lectures/notebooks/3_gaussians.txt
+++ b/lectures/notebooks/3_gaussians.txt
@@ -1,300 +1,300 @@
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diff --git a/lectures/notebooks/Lecture 09 - Neural Networks.ipynb b/lectures/notebooks/Lecture 09 - Neural Networks.ipynb
index a1efcb6..45d92c3 100644
--- a/lectures/notebooks/Lecture 09 - Neural Networks.ipynb	
+++ b/lectures/notebooks/Lecture 09 - Neural Networks.ipynb	
@@ -42,10 +42,6 @@
     "sns.set(rc=rcparams)\n",
     "mpl.rcParams.update(rcparams)\n",
     "\n",
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format ='retina'\n",
-    "\n",
-    "\n",
     "from sklearn.neural_network import MLPClassifier, MLPRegressor\n",
     "from sklearn.metrics import mean_squared_error, accuracy_score, roc_curve, roc_auc_score\n",
     "from sklearn.model_selection import train_test_split, GridSearchCV, KFold"
@@ -173,15 +169,10 @@
     "        value_name=\"score\",\n",
     "    )\n",
     "    fig, ax = plt.subplots(figsize=(16, 8))\n",
-    "    if plot_type == \"bar\":\n",
-    "        ax = sns.barplot(x=x, y=\"score\", hue=\"type\", data=results)\n",
-    "    else:\n",
-    "        ax = sns.scatterplot(x=x, y=\"score\", hue=\"type\", data=results)\n",
+    "    ax = sns.barplot(x=[str(m) for m in results[x]], y=results[\"score\"])\n",
     "    plt.xlabel(x)\n",
     "    if ylim:\n",
     "        plt.ylim(ylim)\n",
-    "    return fig\n",
-    "\n",
     "\n",
     "kfold = KFold(n_splits=5, shuffle=True, random_state=42)"
    ]
@@ -204,11 +195,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/shyue/miniconda3/envs/mavrl/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/shyue/miniconda3/envs/mavrl/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
-      "/Users/shyue/miniconda3/envs/mavrl/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "/Users/shyue/miniconda3/envs/mavrl/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -264,48 +255,12 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
      },
-     "execution_count": 8,
-     "metadata": {
-      "image/png": {
-       "height": 710,
-       "width": 1378
-      }
-     },
-     "output_type": "execute_result"
-    },
-    {
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-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 694,
-       "width": 1001
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
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-      "text/plain": [
-       "<Figure size 1600x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 710,
-       "width": 1378
-      }
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -319,9 +274,7 @@
     "    cv=kfold,\n",
     ")\n",
     "gs.fit(x, y_class)\n",
-    "# plot_grid_search_results(gs, plot_type='scatter').show()\n",
-    "plt.xscale(\"log\")\n",
-    "plot_grid_search_results(gs, plot_type=\"scatter\")"
+    "plot_grid_search_results(gs, plot_type=\"bar\")"
    ]
   },
   {
@@ -335,54 +288,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'<' not supported between instances of 'tuple' and 'int'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/numpy/core/fromnumeric.py:59\u001b[0m, in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbound\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m     61\u001b[0m     \u001b[38;5;66;03m# A TypeError occurs if the object does have such a method in its\u001b[39;00m\n\u001b[1;32m     62\u001b[0m     \u001b[38;5;66;03m# class, but its signature is not identical to that of NumPy's. This\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[38;5;66;03m# Call _wrapit from within the except clause to ensure a potential\u001b[39;00m\n\u001b[1;32m     67\u001b[0m     \u001b[38;5;66;03m# exception has a traceback chain.\u001b[39;00m\n",
-      "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'tuple' and 'int'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[11], line 10\u001b[0m\n\u001b[1;32m      2\u001b[0m gs \u001b[38;5;241m=\u001b[39m GridSearchCV(\n\u001b[1;32m      3\u001b[0m     nn,\n\u001b[1;32m      4\u001b[0m     param_grid\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhidden_layer_sizes\u001b[39m\u001b[38;5;124m\"\u001b[39m: [(\u001b[38;5;241m3\u001b[39m), (\u001b[38;5;241m5\u001b[39m), (\u001b[38;5;241m7\u001b[39m), (\u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m3\u001b[39m), (\u001b[38;5;241m7\u001b[39m, \u001b[38;5;241m5\u001b[39m), (\u001b[38;5;241m7\u001b[39m, \u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m3\u001b[39m)]},\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      7\u001b[0m     cv\u001b[38;5;241m=\u001b[39mkfold,\n\u001b[1;32m      8\u001b[0m )\n\u001b[1;32m      9\u001b[0m gs\u001b[38;5;241m.\u001b[39mfit(x, y_reg)\n\u001b[0;32m---> 10\u001b[0m \u001b[43mplot_grid_search_results\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mplot_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbar\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[0;32mIn[6], line 20\u001b[0m, in \u001b[0;36mplot_grid_search_results\u001b[0;34m(gs, plot_type, ylim)\u001b[0m\n\u001b[1;32m     18\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m16\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m plot_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbar\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 20\u001b[0m     ax \u001b[38;5;241m=\u001b[39m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbarplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mscore\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresults\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     22\u001b[0m     ax \u001b[38;5;241m=\u001b[39m sns\u001b[38;5;241m.\u001b[39mscatterplot(x\u001b[38;5;241m=\u001b[39mx, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscore\u001b[39m\u001b[38;5;124m\"\u001b[39m, hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, data\u001b[38;5;241m=\u001b[39mresults)\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/seaborn/categorical.py:2343\u001b[0m, in \u001b[0;36mbarplot\u001b[0;34m(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, units, seed, orient, color, palette, saturation, fill, hue_norm, width, dodge, gap, log_scale, native_scale, formatter, legend, capsize, err_kws, ci, errcolor, errwidth, ax, **kwargs)\u001b[0m\n\u001b[1;32m   2339\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ax\n\u001b[1;32m   2341\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dodge \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m   2342\u001b[0m     \u001b[38;5;66;03m# Needs to be before scale_categorical changes the coordinate series dtype\u001b[39;00m\n\u001b[0;32m-> 2343\u001b[0m     dodge \u001b[38;5;241m=\u001b[39m \u001b[43mp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dodge_needed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2345\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m p\u001b[38;5;241m.\u001b[39mvar_types\u001b[38;5;241m.\u001b[39mget(p\u001b[38;5;241m.\u001b[39morient) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcategorical\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m native_scale:\n\u001b[1;32m   2346\u001b[0m     p\u001b[38;5;241m.\u001b[39mscale_categorical(p\u001b[38;5;241m.\u001b[39morient, order\u001b[38;5;241m=\u001b[39morder, formatter\u001b[38;5;241m=\u001b[39mformatter)\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/seaborn/categorical.py:384\u001b[0m, in \u001b[0;36m_CategoricalPlotter._dodge_needed\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    382\u001b[0m groupers \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m({\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morient, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcol\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrow\u001b[39m\u001b[38;5;124m\"\u001b[39m} \u001b[38;5;241m&\u001b[39m \u001b[38;5;28mset\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables))\n\u001b[1;32m    383\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhue\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables:\n\u001b[0;32m--> 384\u001b[0m     orient \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_data\u001b[49m\u001b[43m[\u001b[49m\u001b[43mgroupers\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue_counts\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    385\u001b[0m     paired \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mplot_data[[\u001b[38;5;241m*\u001b[39mgroupers, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhue\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\u001b[38;5;241m.\u001b[39mvalue_counts()\n\u001b[1;32m    386\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m orient\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m!=\u001b[39m paired\u001b[38;5;241m.\u001b[39msize\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/pandas/core/frame.py:7267\u001b[0m, in \u001b[0;36mDataFrame.value_counts\u001b[0;34m(self, subset, normalize, sort, ascending, dropna)\u001b[0m\n\u001b[1;32m   7264\u001b[0m     subset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mtolist()\n\u001b[1;32m   7266\u001b[0m name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproportion\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m normalize \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 7267\u001b[0m counts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   7268\u001b[0m counts\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m=\u001b[39m name\n\u001b[1;32m   7270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sort:\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/pandas/core/groupby/ops.py:689\u001b[0m, in \u001b[0;36mBaseGrouper.size\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    684\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m    685\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msize\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series:\n\u001b[1;32m    686\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    687\u001b[0m \u001b[38;5;124;03m    Compute group sizes.\u001b[39;00m\n\u001b[1;32m    688\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 689\u001b[0m     ids, _, ngroups \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroup_info\u001b[49m\n\u001b[1;32m    690\u001b[0m     out: np\u001b[38;5;241m.\u001b[39mndarray \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mlist\u001b[39m\n\u001b[1;32m    691\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ngroups:\n",
-      "File \u001b[0;32mproperties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/pandas/core/groupby/ops.py:729\u001b[0m, in \u001b[0;36mBaseGrouper.group_info\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    727\u001b[0m \u001b[38;5;129m@cache_readonly\u001b[39m\n\u001b[1;32m    728\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgroup_info\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[npt\u001b[38;5;241m.\u001b[39mNDArray[np\u001b[38;5;241m.\u001b[39mintp], npt\u001b[38;5;241m.\u001b[39mNDArray[np\u001b[38;5;241m.\u001b[39mintp], \u001b[38;5;28mint\u001b[39m]:\n\u001b[0;32m--> 729\u001b[0m     comp_ids, obs_group_ids \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_compressed_codes\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    731\u001b[0m     ngroups \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(obs_group_ids)\n\u001b[1;32m    732\u001b[0m     comp_ids \u001b[38;5;241m=\u001b[39m ensure_platform_int(comp_ids)\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/pandas/core/groupby/ops.py:753\u001b[0m, in \u001b[0;36mBaseGrouper._get_compressed_codes\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    750\u001b[0m     \u001b[38;5;66;03m# FIXME: compress_group_index's second return value is int64, not intp\u001b[39;00m\n\u001b[1;32m    752\u001b[0m ping \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroupings[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m--> 753\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mping\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcodes\u001b[49m, np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;28mlen\u001b[39m(ping\u001b[38;5;241m.\u001b[39mgroup_index), dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mintp)\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/pandas/core/groupby/grouper.py:691\u001b[0m, in \u001b[0;36mGrouping.codes\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    689\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    690\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcodes\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m npt\u001b[38;5;241m.\u001b[39mNDArray[np\u001b[38;5;241m.\u001b[39msignedinteger]:\n\u001b[0;32m--> 691\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_codes_and_uniques\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n",
-      "File \u001b[0;32mproperties.pyx:36\u001b[0m, in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/pandas/core/groupby/grouper.py:801\u001b[0m, in \u001b[0;36mGrouping._codes_and_uniques\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    796\u001b[0m     uniques \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_uniques\n\u001b[1;32m    797\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    798\u001b[0m     \u001b[38;5;66;03m# GH35667, replace dropna=False with use_na_sentinel=False\u001b[39;00m\n\u001b[1;32m    799\u001b[0m     \u001b[38;5;66;03m# error: Incompatible types in assignment (expression has type \"Union[\u001b[39;00m\n\u001b[1;32m    800\u001b[0m     \u001b[38;5;66;03m# ndarray[Any, Any], Index]\", variable has type \"Categorical\")\u001b[39;00m\n\u001b[0;32m--> 801\u001b[0m     codes, uniques \u001b[38;5;241m=\u001b[39m \u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfactorize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[assignment]\u001b[39;49;00m\n\u001b[1;32m    802\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouping_vector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sort\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_na_sentinel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dropna\u001b[49m\n\u001b[1;32m    803\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    804\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m codes, uniques\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/pandas/core/algorithms.py:802\u001b[0m, in \u001b[0;36mfactorize\u001b[0;34m(values, sort, use_na_sentinel, size_hint)\u001b[0m\n\u001b[1;32m    795\u001b[0m     codes, uniques \u001b[38;5;241m=\u001b[39m factorize_array(\n\u001b[1;32m    796\u001b[0m         values,\n\u001b[1;32m    797\u001b[0m         use_na_sentinel\u001b[38;5;241m=\u001b[39muse_na_sentinel,\n\u001b[1;32m    798\u001b[0m         size_hint\u001b[38;5;241m=\u001b[39msize_hint,\n\u001b[1;32m    799\u001b[0m     )\n\u001b[1;32m    801\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sort \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(uniques) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 802\u001b[0m     uniques, codes \u001b[38;5;241m=\u001b[39m \u001b[43msafe_sort\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    803\u001b[0m \u001b[43m        \u001b[49m\u001b[43muniques\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    804\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcodes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    805\u001b[0m \u001b[43m        \u001b[49m\u001b[43muse_na_sentinel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_na_sentinel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    806\u001b[0m \u001b[43m        \u001b[49m\u001b[43massume_unique\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    807\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    808\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    810\u001b[0m uniques \u001b[38;5;241m=\u001b[39m _reconstruct_data(uniques, original\u001b[38;5;241m.\u001b[39mdtype, original)\n\u001b[1;32m    812\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m codes, uniques\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/pandas/core/algorithms.py:1593\u001b[0m, in \u001b[0;36msafe_sort\u001b[0;34m(values, codes, use_na_sentinel, assume_unique, verify)\u001b[0m\n\u001b[1;32m   1587\u001b[0m ordered: AnyArrayLike\n\u001b[1;32m   1589\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m   1590\u001b[0m     \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype, ExtensionDtype)\n\u001b[1;32m   1591\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m lib\u001b[38;5;241m.\u001b[39minfer_dtype(values, skipna\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed-integer\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1592\u001b[0m ):\n\u001b[0;32m-> 1593\u001b[0m     ordered \u001b[38;5;241m=\u001b[39m \u001b[43m_sort_mixed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1594\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1595\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/pandas/core/algorithms.py:1667\u001b[0m, in \u001b[0;36m_sort_mixed\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m   1665\u001b[0m num_pos \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m~\u001b[39mstr_pos \u001b[38;5;241m&\u001b[39m \u001b[38;5;241m~\u001b[39mnull_pos\n\u001b[1;32m   1666\u001b[0m str_argsort \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margsort(values[str_pos])\n\u001b[0;32m-> 1667\u001b[0m num_argsort \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margsort\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnum_pos\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1668\u001b[0m \u001b[38;5;66;03m# convert boolean arrays to positional indices, then order by underlying values\u001b[39;00m\n\u001b[1;32m   1669\u001b[0m str_locs \u001b[38;5;241m=\u001b[39m str_pos\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mtake(str_argsort)\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/numpy/core/fromnumeric.py:1133\u001b[0m, in \u001b[0;36margsort\u001b[0;34m(a, axis, kind, order)\u001b[0m\n\u001b[1;32m   1025\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_argsort_dispatcher)\n\u001b[1;32m   1026\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21margsort\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, kind\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, order\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m   1027\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1028\u001b[0m \u001b[38;5;124;03m    Returns the indices that would sort an array.\u001b[39;00m\n\u001b[1;32m   1029\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1131\u001b[0m \n\u001b[1;32m   1132\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1133\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43margsort\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/numpy/core/fromnumeric.py:68\u001b[0m, in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m bound(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m     61\u001b[0m     \u001b[38;5;66;03m# A TypeError occurs if the object does have such a method in its\u001b[39;00m\n\u001b[1;32m     62\u001b[0m     \u001b[38;5;66;03m# class, but its signature is not identical to that of NumPy's. This\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[38;5;66;03m# Call _wrapit from within the except clause to ensure a potential\u001b[39;00m\n\u001b[1;32m     67\u001b[0m     \u001b[38;5;66;03m# exception has a traceback chain.\u001b[39;00m\n\u001b[0;32m---> 68\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/mavrl/lib/python3.9/site-packages/numpy/core/fromnumeric.py:45\u001b[0m, in \u001b[0;36m_wrapit\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[1;32m     44\u001b[0m     wrap \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 45\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m wrap:\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, mu\u001b[38;5;241m.\u001b[39mndarray):\n",
-      "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'tuple' and 'int'"
-     ]
-    },
     {
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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "image/png": {
-       "height": 684,
-       "width": 1331
-      }
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -410,9 +326,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "x_small = data[[c for c in data.columns if c.endswith(\"Mean\")]]\n",
     "nn = MLPRegressor(alpha=1e-4, random_state=1, learning_rate_init=0.01, max_iter=10000)\n",
@@ -450,9 +377,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3/3 [==============================] - 0s 658us/step\n",
+      "20/20 [==============================] - 0s 412us/step\n",
+      "Training MSE = 1.972; Test MSE = 1.723\n"
+     ]
+    }
+   ],
    "source": [
     "x_train, x_test, y_train, y_test = train_test_split(\n",
     "    x_small, y_reg, test_size=0.9, random_state=42\n",
@@ -481,13 +425,6 @@
     "mse_test = np.sum((y_pred_test.ravel() - y_test) ** 2) / len(y_test)\n",
     "print(\"Training MSE = %.3f; Test MSE = %.3f\" % (mse_train, mse_test))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -506,7 +443,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/requirements.txt b/requirements.txt
index 66ca762..7222395 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,5 +2,6 @@ pymatgen==2023.10.4
 seaborn==0.13.0
 scikit-learn==1.3.1
 pandas==2.1.1
+tensorflow==2.14.0
 pytest
 nbmake