diff --git a/datascienceintro/Classification_DecisionTree.ipynb b/datascienceintro/Classification_DecisionTree.ipynb
index d0a19bcea54c06152bc778b3f881f69cc57c34ad..7505fde4fd24aa7e0c93016434c5b409c3c8781e 100644
--- a/datascienceintro/Classification_DecisionTree.ipynb
+++ b/datascienceintro/Classification_DecisionTree.ipynb
@@ -1,6 +1,7 @@
 {
   "cells": [
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "7cDlwRScAZa9"
@@ -10,7 +11,7 @@
         "\n",
         "In this example, we look at the performance of a simple [decision tree](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) from the Scikit-Learn package.\n",
         "\n",
-        "We will use the [adult](https://archive.ics.uci.edu/ml/datasets/adult) that focuses on a (binary) classification task whether or not a person makes more than 50k USD per year. The data are taken from a 1994 census and were first discussed in the paper [Ron Kohavi, \"Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid\", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996](https://www.academia.edu/download/40088603/Scaling_Up_the_Accuracy_of_Naive-Bayes_C20151116-5477-1fw84ob.pdf)\n",
+        "We will use the [adult dataset](https://archive.ics.uci.edu/ml/datasets/adult) that focuses on a (binary) classification task whether or not a person makes more than 50k USD per year. The data are taken from a 1994 census and were first discussed in the paper [Ron Kohavi, \"Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid\", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996](https://www.academia.edu/download/40088603/Scaling_Up_the_Accuracy_of_Naive-Bayes_C20151116-5477-1fw84ob.pdf)\n",
         "\n",
         "The data have a number of categorial and numerical features.\n",
         "We can access the data directly from the archive (or use a local copy)."
@@ -18,7 +19,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 124,
+      "execution_count": 1,
       "metadata": {
         "id": "khH_Ht3WS2hp"
       },
@@ -48,7 +49,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 2,
       "metadata": {
         "id": "fcQJAxNJTdv1"
       },
@@ -78,7 +79,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -91,10 +92,7 @@
         {
           "data": {
             "text/html": [
-              "\n",
-              "  <div id=\"df-04d645ad-76e3-46c8-a19d-23dc0407acb7\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
+              "<div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -222,83 +220,7 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04d645ad-76e3-46c8-a19d-23dc0407acb7')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
-              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-04d645ad-76e3-46c8-a19d-23dc0407acb7 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-04d645ad-76e3-46c8-a19d-23dc0407acb7');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
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-              "    </div>\n",
-              "  </div>\n",
-              "  "
+              "</div>"
             ],
             "text/plain": [
               "   Age          WorkClass  fnlwgt   Education  EducationNum  \\\n",
@@ -323,7 +245,7 @@
               "4            0            0            40            Cuba   <=50K  "
             ]
           },
-          "execution_count": 16,
+          "execution_count": 3,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -352,7 +274,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 49,
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -364,21 +286,19 @@
       "outputs": [
         {
           "data": {
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nZlgCOC3fz3oLeBk4PO87GDhX0rKk+36HdLTRiHhY0rHALZIGAO+QLj9WL+L3W9LlxAclvQOcFxG/lnQIcFkOUFNI9wI79V0RMUnSz0mj1VeBR4E5+bBvAWdJepD0W9xROG8zM2uCTi/qLGkc8AnSxA3fL+qAyn23HDCvAn4XEVc12FZ4rUvrbmN3GY7/792tvKhzD2rk0uUxwFKkUdbyTe5PWR0vaRowgzQj9epe7o+ZWb/RyKXLy4A3SZM4DpT0OPDPGvUiInbqSufKIiK+29t9MDPrrxoJdNsXtpcjTRypxdc9zMys1znxaovplufoNABiQdObtdblxKvdzvfoelCfWOvSOqfeZJRGJ4Z44oGZlVmfyV5gZmbWHRoOdJL2l/QXSf+Q9K6kVyXdKmn/ZnbQzMysKxpZ61KkhZYPJF1nnk96wHsV0nqPO0r6j4iotXiymZlZj2pkRPefpAwA95EWJV46IlYDls6f7wX2l+QVPszMrNc1EugOBZ4Gto2I2yJiPqQMBBFxG7Bd3n9YszpZkXPTRc500DIkbS/put7uh5lZf9RIoNsAuCoi/l1rZy6/GvhoVzpWxwHA/+Z3MzOzdjUS6IL2nwFp+jMi+d7gfsBo4NM5MSqSlpN0vaQHcobwUbn8JEkP54zhp+WyYZJuy2UTlLOgS1pVKXv5A/m1VY3vHynpbznj+N2ShufyetnJd5P0qKT7gM8V2jle0kWS7pQ0S9LnJJ2ilMX8JklLNPtvZ2bWnzUS6B4BPidpmVo7c/newMNd6VgNW5ESof4dmAh8JpfvBjwfERtHxIbATZLeB+wDfCxnDD8x1z2TlDNuI+APwK9y+a9IyVE3BjYFHqrx/Y8C20TEJsCPgZ/n8nrZyc8D/oOUjPWDVW2tC+wIfBb4PXB7zmL+78J5LeQM42ZmjWsk0P0OWBO4Q9JOhRxuAyXtANwOrJXrNdMBwKV5+1IWXb6cThrhnSxpm5zDbg4p3c/5kj5HWpsT4JPAJXn7f0gZyCEFnXNg4b3GShqdosGklD0zSAlhP5bLdwZ+U8xODqxPCsqP5wwPv69q68aIeCf3fSBwU+FchlV/cUSMi4jNI2Lzun8dMzOrqZFA9xvgj6SRyi3AvyW9SAosfwFGkhKz1s3d1lmSBpISlv5Y0tOkkdluklaIiMdIo7DpwImSfpyDzkjgcmBPFgWSznzn1yVNy68PAT8ljbw2JI3Ulu7CKc0DiIgFwDuFdEeVzOhmZtYknQ50kRxEesTgNtLoaeX8fhtwUEQ0+6HxnYAHI2JoRAyLiLWAK4B9chB6MyJ+D5wKbJrTBw2OiBuAI4GNczt3A5W+HQTcmbcnAEfAwpHp4Ig4q5CF/HnSiO65XH90oW+1spM/SspOvm6u48kzZma9pOHRQ0T8kTSy6wkHkBKWFl1BCk4vAqdKWkDK6n0EsALw53yvTMDYfMw3gQskHUV6yL2SrfzbwDhJh5EegD8C+FvV950CXJSziF9fKK+XnXwMcL2kN0kBdYWu/AHMzKwxzl7QYtrKMO5Fnc1ahrMX9CAv6mxmZqXW7qXLfElwAbBBRDyWP3fkn/8REZ5YYWZmvaojgegOUmB7s+qzmZlZn+d7dC2mWzKM1+Es02bdxvfoepAvLbagepNRKhqdlFJt7C7Du9yGmVlv6/RkFElPSvpWO3W+LunJxrtlZmbWHI3MuhwGDGmnzhDSMmBmZma9qrseL1gBeLub2jYzM+uwDt2jq6SzKRhSowzSAsVrktal7LVLl5JGAPcDu0dEp9e5NDOz8ujoZJSnWfyRgm/nVz3FZbd6QzFBqwOdmVk/1tFAdzGLEq5+GXgQmFaj3nzgH8CEiLilKT3spEKC1k8Dd0paOiLekrQc8CdgDdLI86cRMV7SSaS8cO8Ct0TEdyUNI6UZWoW8JmZEPCNpVeBcYJ38dUdExN2F714fuDgiRubPw4BrI+LjknYCTiP9zafkY+dJ2gL4JbAcKavBThHxRjf9eczM+p0OBbqIGF3ZlvRl4KqI+El3daqLFiZolTSRlMj0ChYlaP0MgKTBhQSt60dESKpMsqkkaL1I0qGkxKx7syhB6z45ddDyxS+OiEclLSlp7Yh4ChgFjM+LS19ICmKPSboYOELS2cB4YFRETJG0Iin56mLyAtFjmvg3MjPrNxpJ0zOgDwc56P0ErX8iBTjy+3hgOCn4PpbLLwK2zeUvRMSU3ObrlQSuRU68ambWuFIt6txHErSOB74g6SOk9T4fb8rJmZlZQxpeGSXfW9oVWB1YqkaViIjDGm2/QZUErbtWCiRdRErQ+hfg1Yj4vaR/Al/JCVqXjYgbJN3FopmilQSt/0PtBK2/qFy6jIizgLOKnZA0H/gRKegBzCQlYl0vIp4AvgT8NZevJmmLfOlyBeDftUZ1ZmbWmE4HujzZ40Lgi6TJKZVJKhVRKO/pQNcXErRCCnCnAmsD5MkwhwCX5UzkU4BzI+JtSaOAMyUtQ7o/tzMwt2t/BjMzq2hkRPcN0ojkYtLkjKnAL0j3prYHjgZuAI5pThc7LiIOqVF2DXBN/nhzjcNG1jhmFul+XHX5i8BeHejHaaQZlsWyCcAmNepOAbZsr00zM2tMI4HuYGBmZSZmGuDxz4iYBEySdDMwCbgVuKBJ/TQzM2tII5NR1gduqypbGDAj4n7gOuBrXeiXmZlZUzQ667I4rf5fwMpV+x8nBUQzM7Ne1enEq5JmApMi4uD8+QHgzYj4ZKHOFcDWEfHBZnbWmpB4VQMgFnSoqhOvmnUbJ17tQY3co5sMbFb4fCNwlKQfAVeSJqTsRbp8ad1g8NYHNpxYdewuw3FWeTOTNDcilm+/Zutr5NLlFcBASWvnz6cAs4ATSGtgngn8kzT70szMrFd1ekQXEVcDVxc+vyppE+CrwLqkTAcXR8QLzeqkmZl1D0nbA8cDrwAbAvcCX8zr/75n0XnSc8jnAJuTFsMfGxG3SxpNWhN4OeDDpEesliQ9jjYP2CPHi3VJi2y8n7Ts4lcj4tHuPMeGV0Ypyms+ntZuRTMz64s2AT4GPA/cBWwtaTK1F53/Nmnlq4/njC235CUPIQXKTYClgSeA70fEJpLOIGW++QUwDjg8Ih6X9AngbGo8t9xMTQl0ZmbW0iZHxGwASdOAYaTZ9YstOp/3f4p0i6qSsWUWUAl0t+c0Y29ImgNcm8unAxvlZRe3Iq0SVfnuWktINlWn79FJOlzS3/MCxrX2r5739/TyXw2RtEJhUeZpkl6R9Iu8b29JGxTqTpTUbgYBSSMkTcrtTZVUyU+3vaStCvUulLRvd5yXmVknzCtsz6fxQVCxnQWFzwtymwNIC4yMKLw+2uB3dVgjk1EOJEX552vtjIjngNmktTD7vIh4o/hHJ02suTLv3hvYoP7RdZ0CnJDb+3H+DGlG6lb1DjIz60MWLjoPCwcFg0iL3B+Uyz4CrJnrtiuPCp+StF8+XpI27o7OFzUS6IYDD7RT50Fa8IHx/KN9gJSZfCtS5vFT88hs3VxtP0mTJT0maZs6TQWwYt4eDDyfs40fDhyZ26scu62kuyU96dGdmfUVEfE2Kafmmfl56VtJ997OBgZImk66hzc6IubVb+k9DgIOy20+RAfWD+6qRoang0mPD7TldWClBtrubfsD4yM9aHa3pGuA6yLicli4ruegiBgpaQ/gOFK2gWrfAW6WdBrpHxNbRcQsSecCc/Oiz+TLu6uREruuT1p8+vLqxpxh3MyarfIMXURMBCYWyr9R2K636HytBfQvJGW2qXweVmtfRDwF7NaFrndaIyO6F4CN2qmzESm9TavZH/hjO3UqlzXvJd2wreUI4MiIGAocCZzfRntXR8SCiHgYWLVWBWcYNzNrXCOB7nZS1u5P1dqZL8ntTkpS2jLydeJBEXFvO1UrQ/SFN2wlXZAvR96Q9x3MooB4GTVSAdVoD7wskJlZ0zVy6fJk0nXbv0g6G7gJeI6UaXx30mhmXq7XSg7gvaO5N0jJWdtUIw/e88B2pMsBO5IWua60tyJmZtZjGlkZZaakLwCXkO5FfbuwW6T7cwdGxCPN6WKP+QKwR1XZpcB5kr4FdGaiyFeBX+YZSm+x6P7atcDlkvYiZTE3M7Nu1tCzEhFxvaR1gNHAJ4AhpAkqk4CLIuIfTethD4mIdWqU3cXijxdsX9j3CnXu0UXE/7L4wteV8sdY/P7mnVX7+8UCq2ZmPanhlVFyMPvvJvbFzMys6RpNvGpmZr1IAwfNlhRNew0cNLtH+y8Nk/TvwqpU5xb2bSZpuqQnJP1K+dmu4mpSklaWdL+k9zzqUK3TIzpJ23a0bkTc0dn2zcysAxbMX32t7193QrOam3Xyno0luSyQNABYIS/03xF/zytIVTuHNNfhHuAG0nN3Nxa+ZzBwMzAuIi5o70sauXQ5kbTyR0cMbKB9a8ecuy5h7C6XdCpbeMUaQ9fspl6ZWX8laS3SQ+QHkSYpXt+FtlYDVoyISfnzxaTlGCuBbvm8fUlEnNORNhsJdD+hdqAbAmxBWsvxWuC+Btq2Djh9XHr+fOyYw4jbfr7YPu34A2cQN7NuJ2lJ0vJdXyEtnXgR8Mk8UQ9JR5HXxKxyR0R8K2+vLel+0mz9YyPiTtKjasXLqLNzWcXpwG8j4oyO9rWRxwuOb2u/UvK9M4EfdrZtMzNrGVNJMeSQiLinemdEnAqc2sbxLwBrRsQ/JG0GXC3pYx343tuAvSSdFhEvdaSjTZ+Mktc0mwT8vJ2qZmbWur4K/A34vaRTJC2WbkfSUVUp0CqvXwFExLzKo2h5Raq/k/LaPQesUWhqjVxWcSlwLnCDpHYX9IDuS7w6jfRHMDOzEsqjuHuUkqmOAs7Pk1G+FhH3tTeik/R+4NWImJ+fy/4w8GREvCrpdUlbkiajfJmc6LXw3WdI+iBwpaTP5EwLdXVXoBvajW23SdLTpKW2IE2GuRI4MSLeaqCt0cAt9XLvNdDe3sBjeQFnM7PGDRj4XDNmShbba+SwiJhLWrj+/OpRXTu2BX4i6R1SYtbDI+LVvO9rpGwHy5AmntxYfXBEfF/SBcD/SDogov7MvKYGI0kDSTNv9gX+t5ltd9IOEfFK/pfGOOA3pIWWO2s0MIO0dmWHSBoYEfPr7N4buA5woDOzLon5767Rfq2e1ZmlHyPiCuCKOvumAhvWKB9d9bndZ+igsefonmyjrVXz+9vADzrbdrNFxFxJhwPPSloZeI2U7Xt30szREyNiPICk75Oyoi8g/ethKrA58AdJ/wY+SZpRehrpHKcAR0TEvDyKHA98GjglXzceAywJPAF8CRhBSuS6naRjgc/nbp4FvB94E/hqRDzafX8RM7P+p5ER3QBqP17wDjAdmAyc2VcWdY6I1yU9Rbr+uwYp4GwMrAJMkXRHLtsL+EREvClp5Xyd+BvAdyNiqqSlSUPpnSLisfxsxxHAL/JX/SMiNgWQ9L6IOC9vnwgcFhFn1kjkOoE0XH9c0idImXt3rD4HJ141M2tcI48XDOuGfnS3Sp63TwF/zJcWX5T0V9Kzf9sBF0TEmwCF68RFw4Gn8sLMkJ4Z+TqLAt34Qt0Nc4AbQnq48eb3dChdVt0KuCyvbgOwVK3OR8Q40iVYJPkhOTOzTuiVCSM9KV9GHAY81k7VrvpXYftCYO+IeCBPaNm+Rv0BwD/rLH9jZmZNUupFnfOo6Wzg6oh4jZQWZ5SkgXlq67akS623AodIWjYft3Juoph4dSYwTNJ6+fOXgL/W+eoVgBckLcHiKwMsbC8iXgeekrRf/k7lLOdmZtZE7Y7oJP24wbYjIn7a4LFddXte7XoAcBVQ6cdVpEklD5DuM34vIv4PuEnSCGCqpLdJi4j+gDQyO7cwGeUQ0qXGymSUc6ntR6TnP17O75VgWZ3I9SDgnDw5ZYm8/4Gm/AXMzAwAtbcuoqRazyYUD1KNcpECnRd1bjJJ4bUuzVqe2q/StiUGava7CxZbA7JLBg3guXfmR9MeWZB0PGnhkJdz0Q8i4oa87xjgMGA+8K2IuDmXz60koJa0B2kOxKcjYlZX+tKRe3Q71Cg7EtgD+AMpm8H/AR/MdQ8krVz9ixrHmZlZE7y7gNXjuBWblqZHJ7zeqYfPJa2Ubwm15YyIOK3quDE167oAABUtSURBVA2A/YGPAR8C/iLpI8XnjyXtBPwK2LWrQQ46EOgiYrH7UJK+THpebMuIqM5QcJGkXwN3kFYkMTOzcrpa0hzgt8ANEfFuB4/bC7g0IuaR5ik8AYwkrZtZyXl6HrBHRPy9GR1tZDLKkcD4GkEOWPhE+59yPTMzK6ftSSlz9gUekfTzwmS9im9IelDS7yStlMtWB54t1Cmm4VkKuJo0a71pi2c08njBcNJkjbY8D+zXQNvWAWPHHLZwWzsd+57kq5Xn8tYaujpPPzMbM7NmizQZYCIwUdKKwPeBRyWNyst7nUOaCBj5/b+BQ9tp9h3gbtL9u283q6+NBLrXga3bqfMpYG4DbVsHVCajQO0JKRXasddXYTOzEpO0DLAPKYANIQWnWwEi4sVCvfNI6/xCSrkztNBMMQ3PAuALwARJP4iIpqR7a+TS5fXANpJOq84FJGkFSf9NCoTXNqODZmbW90g6hbRA/VbAURGxeUSclZ8RRtJqher7kBbIB7gG2F/SUpLWJi3POLlSMa9Q9RngIEmH0QSNjOiOIV2bPRL4iqRpwIukBZ1HACsCT9IHFnU2MyurQQN4rrMzJdtrr5OHTAR+3EYKtFPy88kBPA38J0BEPCTpT6Qg+S7w9eqML3mt4d2AOyS9HBHXdLJvi2lkrcuXJI0E/ov0KMG2hd1vkmbL/KCSOdbMzJqvmc+8NaLyTFwb+7/Uxr6fAT+rUb58YftZYO2u9LGiobUucxAbI+lrwPrAYGAO8GgnppiamZl1uy6tdRkR70bEjIi4K7/3aJCT9LSk6Xn66i05tXpnjj9V0kOS6qZ770AbEyVt3ujxhXa2l3Rd+zXNzKwzOhToJG0rac2ONippo/xgeU/YISI2IiVKXey+YF4oua1zHANsFBFHdWcHzcys93R0RHc7MLpYIOn7kurdh9sHuKAL/WrEHcB6koZJmpkTo84AhuaR24w8+hsFkJOgLg/cWymrkLSdpGn5dX9ldmk+5+mSHpB0UuGQ/SRNlvSYpG1y3aUlXZDr3y9ph7bKzcyse3T0Hl2tBUiXJj030VfsScpwDmm66sERMUnS56mRVTwiPpsXEK2VD+67pJlAd+VUP29J2p2qLOSF+oMiYmRehPQ4YGdSUtaIiI9LWh+4RdJH2iivS84wbmbWsDLko7s9P+KwImkmKMCsiJiUtxdmFc8PMFayirflLuD0nE5nSL73uDP1s5BX1vW8l5TktfK9v891HwVmAR9po7yuiBiXn1Hp8r1AM7P+pgwZxneIiFcqHyQNYfFs3+2S9HVSOglIC4meJOl6UoaGuyTt2k4T8/L7fMrxNzUzK40yjOjaUy+r+EL5af4R+fW8pHUjYnpEnExKsLo+9bOQt/W9B+W6HwHWJGUpr1duZmbdoD+MPuplFW/Ld/IkkQXAQ8CNETFPtbOQ13M2KXv4dNLT/6NzG/XKu3KOZmZWR2cCXZ9LWx0Rw2qUPQ1sWPgcwFH5VV13+eqyXP7NOuUnASdVlW1f2H6FfI8uL4tzSI026pVPJC2pY2ZmTdSZQHd8To2+GEnza9Q1MzPrEzoT6Dp7ba3PjQDNzKz/UbqyZ61CUts/mAYsTMTqxKtmfZZvyveg/jAZpXROv6X+JM2xuwzH/3gxM1ukPzxeYGZm/ZgDnZmZlZoDnZmZlZoDnZmZlZoDHQuTp84spOb5QC7fW9IGVfU6tLCypG9KejQndj0ll43IGQ4qdY6X9N1mn4+ZmS3iWZeLHBQRU6vK9gauAx7uTEN5+bC9gI3z8l4fyLtGAJuTlg8zM7Me4BFdHZK2Aj4LnJpHeevmXe9JslrDEcBJETEPICJekrQk8BPSAtPTCsleN8gjxSdzWiAzM2siB7pFLsgB6EeSFBF3A9cAR+WsBn/P9QZFxEjgO6Qkq7V8BNhG0j2S/ippi4h4G/gxMD63Nz7XXR/YFRgJHCdpierGJI2RNFVS9YjTzMza4UCXHBQRHwe2ya8vtVG3VpLVaoOAlYEtSYtJ/0n10xNcHxHz8oLQLwGrVldw4lUzs8Y50AER8Vx+fwO4hDS6quc9SVYlVUaDlXtvs4ErI5lMSvezSjvtLdammZk1R7//j6qkQcCQiHglXzbcE/hL3v0GsEJ7bUREddqdq4EdgNtzctUlgVc62p6ZmTWPR3SwFHCzpAeBacBzwHl536XAUZLuL0xG6YjfAetImpHbODjnxbudNPmkOBnFzMy6kbMXtBhJ4UWdzVqesxf0II/ozMys1BzozMys1BzozMys1HyPrsW0m2G8C9YYuibPPjOru5o3s0V8j64H9fvHC1rR4K0P5Ljj6i3K0rixuwxveptmZr3Nly7NzKzUHOjMzKzUHOjMzKzUHOi6QNLPJD0raW5V+VKSxkt6ImcwGJbLnXjVzKyHOdB1zbXUXgD6MOC1iFgPOAM4OZePAPaoUd/MzLqJA10XRMSkiHihxq69gIvy9uXATpKWwolXzcx6nANd91gdeBYgIt4F5pCyFjSUeNXMzBrnQNf72k286gzjZmaNc6DrHs8BQ2FhvrvBwD/q1G038aozjJuZNc6BrntcAxyct/cFbsv56Jx41cyshznQdYGkUyTNBpaVNFvS8XnX+cD7JD0BjAWOzuVOvGpm1sO81mUXRMT3gO/VKH8L2K9G+avAFm20t2FTO2hmZh7RmZlZuTnQmZlZqTnQmZlZqTnxaot5T+JVDYBY0JS2nXjVrMc48WoP8mSUFnT6LTMXbo/dZTj+x4qZWX2+dGlmZqXmQGdmZqXmQGdmZqXmQGdmZqXmQJfVyxZep+5imcLbqPerYnuS9pa0QeHzREleqNnMrBs50C1SL1t4Le1mCs8BbKWq4r2BDWpUNzOzbuJAl9XLFi5pP0kzJD0g6Q5JS1I7U3jxmIHAqRTWwZS0FfBZ4NR83Lp5136SJkt6TNI23XJyZmb9mJ+ja9+PgV0j4jlJQyLibUk/BjaPiG/UOeYbwDUR8YKUnguNiLslXQNcFxGXA+R9gyJiZL4Uehywc3VjksYAY5p+ZmZm/YBHdO27C7hQ0leBge1VlvQhUuaCMzvY/pX5/V5gWK0KTrxqZtY4j+jaERGHS/oE8BngXkmbVdeRdDOwKjAVuApYD3gij9iWlfRERKxX5ysqGcZrZhc3M7Ou8X9Y2yFp3Yi4B7hH0u7AUKoyhUfErlWHfbBw/NxCkHOGcTOzHuZLl1kb2cJPlTRd0gzgbuABGs8UfilwlKT7C5NRzMysGzl7QYuRFF7U2azlOXtBD/KIzszMSs2BzszMSs2BzszMSs336FqMpChmFXdWcLOW5Ht0PciPF7SiWOAJKGZmHeRLl2ZmVmoOdGZmVmoOdGZmVmqlDXSSns4rmjwo6RZJH2z/qIa+Z7SkXzepraclrdKMtszMLCltoMt2iIiNSIst/6C4Q0nZz9/MrN/rL/+hvwNYT9IwSTMlXQzMAIZKOkrSlDzyOwFA0nKSrs/JVmdU1rOUtIWku3P5ZEmVBZo/JOkmSY9LOqXypZIOqKyTKenk9srNzKz5+svjBXsC0/P2h4GDI2KSpF3y55Gk51qukbQt8H7g+Yj4DICkwTmz+HhgVERMkbQi8O/c5ghgE1LKnZmSziSl3TkZ2Ax4DbhF0t7A5FrlEXF19/4JzMz6p7KP6G6XNA1YEfivXDYrIibl7V3y637gPmB9UuCbDnxa0smStomIOcBw4IWImAIQEa9HxLu5nQkRMSci3gIeBtYCtgAmRsTLud4fgG3bKK9L0hhJUyVN7fqfxMysfyn7iG6HiHil8kHSEOBfhf0C/isiflN9oKRNgT2AEyVNICVUrWdeYbvpCVQjYhwwLvfLT4qbmXVC2Ud07bkZOFTS8gCSVpf0AUkfAt6MiN8DpwKbAjOB1SRtkeuuIKmtgDYZ2E7SKpIGAgcAf22j3MzMukHZR3RtiohbJH0U+JskgLnAF4H1SAlXFwDvAEdExNt5UsqZkpYh3Z/buY22X5B0NClJq4DrI+LPAPXKzcys+byoc4upXLr072bW0ryocw/q75cuzcys5BzozMys1BzozMys1BzoWpSkNl9D11yrt7toZtYn9OtZl61q8NYHctxxx7VZZ+wuw3uoN2ZmfZtHdGZmVmoOdGZmVmoOdGZmVmoOdGZmVmoOdF0g6WeSnpU0t6p8rKSHc467CZLWyuXDJB1YqNe07ORmZlabA13XXEvKZVftfmDznN38cqCSjHUYcGCN+mZm1k0c6LogIiZFxAs1ym+PiDfzx0nAGnn7JGAbSdMkHZnLamYnNzOz5nCg636HATfm7aOBOyNiRESckctGAKOAjwOjJA2tbsCJV83MGucHxruRpC8CmwPbtVFtQs5gjqRKdvJnixWceNXMrHEOdN1E0s7AD4HtImJeG1W7NTu5mVl/5/+odgNJmwC/AXaLiJcKu94AVuidXpmZ9U++R9cFkk6RNBtYVtJsScfnXacCywOX5Ykn1+TyB4H5kh4oTEYxM7Nu5BFdF0TE94Dv1SjfuU79d4Adq4ovLOzfs5n9MzMzj+jMzKzkHOjMzKzUHOjMzKzUFOHHslqJNCCg/d9sjaFr8uwzs3qgR2bWAPV2B/oTj+hazGabbUpEtPtykDMzSxzozMys1BzozMys1BzozMys1BzozMys1BzozMys1BzozMys1BzozMys1BzozMys1BzozMys1BzozMys1BzozMys1BzozMys1Jy9oMVIegOY2dv9aKJVgFd6uxNNVLbzgfKdU184n1ciYrde7kO/Mai3O2CdNjMiNu/tTjSLpKk+n76tbOdUtvOx9vnSpZmZlZoDnZmZlZoDXesZ19sdaDKfT99XtnMq2/lYOzwZxczMSs0jOjMzKzUHOjMzKzUHuhYhaTdJMyU9Ieno3u5PPZKGSrpd0sOSHpL07Vy+sqRbJT2e31fK5ZL0q3xeD0ratNDWwbn+45IO7q1zyn0ZKOl+Sdflz2tLuif3e7ykJXP5UvnzE3n/sEIbx+TymZJ27Z0zWdiXIZIul/SopEckfbKVfyNJR+b/vc2Q9EdJS7f6b2RNFBF+9fEXMBD4O7AOsCTwALBBb/erTl9XAzbN2ysAjwEbAKcAR+fyo4GT8/YewI2AgC2Be3L5ysCT+X2lvL1SL57XWOAS4Lr8+U/A/nn7XOCIvP014Ny8vT8wPm9vkH+3pYC18+85sBfP5yLgK3l7SWBIq/5GwOrAU8Ayhd9mdKv/Rn417+URXWsYCTwREU9GxNvApcBevdynmiLihYi4L2+/ATxC+g/RXqT/uJLf987bewEXRzIJGCJpNWBX4NaIeDUiXgNuBXplJQlJawCfAX6bPwvYEbg8V6k+n8p5Xg7slOvvBVwaEfMi4ingCdLv2uMkDQa2Bc4HiIi3I+KftPBvRFr8YhlJg4BlgRdo4d/ImsuBrjWsDjxb+Dw7l/Vp+ZLQJsA9wKoR8ULe9X/Aqnm73rn1pXP+BfA9YEH+/D7gnxHxbv5c7NvCfuf9c3L9vnQ+awMvAxfky7G/lbQcLfobRcRzwGnAM6QANwe4l9b+jayJHOisW0haHrgC+E5EvF7cFxEBtMRzLZL2BF6KiHt7uy9NNAjYFDgnIjYB/kW6VLlQi/1GK5FGY2sDHwKWo/dGltYHOdC1hueAoYXPa+SyPknSEqQg94eIuDIXv5gvd5HfX8rl9c6tr5zz1sBnJT1NumS8I/BL0uW7ylqxxb4t7HfePxj4B33nfCCNVGZHxD358+WkwNeqv9HOwFMR8XJEvANcSfrdWvk3siZyoGsNU4AP51lkS5JuoF/Ty32qKd/rOB94JCJOL+y6BqjMyjsY+HOh/Mt5Zt+WwJx8+exmYBdJK+V/se+Sy3pURBwTEWtExDDS3/22iDgIuB3YN1erPp/Kee6b60cu3z/P+Fsb+DAwuYdOYzER8X/As5KG56KdgIdp0d+IdMlyS0nL5v/9Vc6nZX8ja7Leng3jV8depJlvj5Fmgv2wt/vTRj8/Rbrk9SAwLb/2IN0DmQA8DvwFWDnXF3BWPq/pwOaFtg4lTQh4AjikD5zb9iyadbkO6T+CTwCXAUvl8qXz5yfy/nUKx/8wn+dMYPdePpcRwNT8O11NmjXZsr8RcALwKDAD+B/SzMmW/o38at7LS4CZmVmp+dKlmZmVmgOdmZmVmgOdmZmVmgOdmZmVmgOdmZmVmgOdmZmVmgOdWT8gabSkkDS6t/ti1tMc6MzMrNQc6MzMrNQc6Mw6QdLInJ36OUnzJL0g6RZJX6iq9wVJd0iaI+nfkqbn7NVL1WgzJE2s830X5v3DCmXDctmFeftSSa9IekvS1JxxodjGROCC/PGCfGxUt2tWVoPar2JmAJK+CpwDzCctAPw48AFgc1LW6j/lej8HjgFeIWUlnwvsDvwc2FXSLpES6HbVWqS1Gp8kre+4MjAK+LOknSPi9lzvQqCSWPXPpPVHK/7ZhH6Y9WkOdGYdIGkD4GzgdWCbiHioav8a+f2TpCD3LDAyUqYAJB0DXAXsCXyXFPS6anvg+Ig4odCPS4CbgKNIq/cTERemRf3ZC7g6Ii5swnebtQxfujTrmCNI/zD8aXWQA4iI2Xnz0Px+YiXI5f3vAv+PlKX8K03q0yzgxKp+3ExKWzOySd9h1vIc6Mw6Zsv8fmM79TbN77dV74iIx0hJT9eWNLgJfZoWEfNrlD9LSrtjZjjQmXXUkPzeXsbpSgB7oc7+SvmQOvs7o979tXfx/7fNFvL/Gcw6phJUVm+n3pz8/sE6+1erqgcpUW29++XNCIhm/ZoDnVnHTMrvu7dT7/78vn31DknrAWsAT0VEcTT2GjC0Rv2BpEzgzVC5xDmwSe2ZtQwHOrOOOYd0SfBHeQbmYiqzLoHf5fdjJb2/sH8gcBrp/3PnVx0+GVhT0i5V5ceSHiFohn/k9zWb1J5Zy/DjBWYdEBEPS/oacC5wv6Q/k56jex+wBemxgx0i4m5JpwDfA2ZIuhz4F2kkuCHwv8CpVc2fBuxKev5tPPAqsBWwNjCRGqPDBvwNeBP4jqT3AZUZoWdGxJz6h5m1Po/ozDooIs4DPgVcRwo+RwGfBV4GzirU+z5wACkQfhn4Fun/a8cCn65+WDwiJgB7Aw8B+wMHA0+THhGY1aS+vwZ8HngYGA38NL88O9NKTxHR230wMzPrNh7RmZlZqTnQmZlZqTnQmZlZqTnQmZlZqTnQmZlZqTnQmZlZqTnQmZlZqTnQmZlZqTnQmZlZqf1/erJAO+dbBPwAAAAASUVORK5CYII=",
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",
             "text/plain": [
-              "<Figure size 434.875x360 with 1 Axes>"
+              "<Figure size 604.125x500 with 1 Axes>"
             ]
           },
-          "metadata": {
-            "needs_background": "light"
-          },
+          "metadata": {},
           "output_type": "display_data"
         }
       ],
       "source": [
-        "sns.displot(data=df,y='Education',hue='Income')\n",
-        "plt.xlabel('count', size = 20)\n",
-        "plt.ylabel('Education', size = 20)\n",
+        "sns.displot(data=df,y='Education', hue='Income')\n",
+        "plt.xlabel('count', size=20)\n",
+        "plt.ylabel('Education', size=20)\n",
         "plt.show()"
       ]
     },
@@ -426,13 +346,6 @@
         "Note the strong peak at 40 hours (default work week), so use a log-scale for the ```y```-axis."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "veqQO6JPMd6T"
-      },
-      "source": []
-    },
     {
       "cell_type": "code",
       "execution_count": 54,
@@ -929,12 +842,13 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "LbwnPZBfH1VC"
       },
       "source": [
-        "To follow best practices, we keep split the data into separate samples that we use for training and evaluation of our model.\n",
+        "To follow best practices, we split the data into separate samples that we use for training and evaluation of our model.\n",
         "The test data are only used in the evaluation to be able to verify the performance on an independent sample."
       ]
     },
@@ -1650,13 +1564,14 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "RRuY_qcgLfcx"
       },
       "source": [
         "A key plot to understand the performance is the ROC curve.\n",
-        "Here, we need probabilities and the curve is constructed by setting subsequent thresholds on the predictied probabilies.\n",
+        "Here, we need probabilities and the curve is constructed by setting subsequent thresholds on the predictied probabilities.\n",
         "A model that is only as good as random guessing would lie on the diagonal, the ideal point is (0,1) where all predictions are perfect."
       ]
     },
@@ -1718,7 +1633,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0]"
+      "version": "3.10.6"
     },
     "vscode": {
       "interpreter": {
diff --git a/datascienceintro/DataScience_Stats_Correlation_2Dice.ipynb b/datascienceintro/DataScience_Stats_Correlation_2Dice.ipynb
index 92948a5737fbbf7583fcecc02843243eab909221..ecd87192a72b3f9cdfd9ca39def25e1f8828f332 100644
--- a/datascienceintro/DataScience_Stats_Correlation_2Dice.ipynb
+++ b/datascienceintro/DataScience_Stats_Correlation_2Dice.ipynb
@@ -1,31 +1,17 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "gCrmxVOcGaD6"
+      },
       "source": [
         "# Spurious Correlation\n",
         "\n",
         "In this simple example we investigate how the the way we record data can have a large impact on the conclusions we can draw from data.\n",
         "\n",
         "We use the example of two simple dice and simulate rolling the dice with the random number generator in NumPy ```np.random.choice``` that returns one of the numbers listed in the arguments randomly."
-      ],
-      "metadata": {
-        "id": "gCrmxVOcGaD6"
-      }
+      ]
     },
     {
       "cell_type": "code",
@@ -42,27 +28,20 @@
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "dL0XzGaak6DB"
+      },
       "source": [
         "# Two (simple) Dice\n",
         "\n",
         "Imagine we have two six-sided dice and roll them.\n",
         "If we either roll them one after the the other, or both at the same time:\n",
         "In either case we expect that each number appears with the same frequency and that there is no dependency between them."
-      ],
-      "metadata": {
-        "id": "dL0XzGaak6DB"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "# roll the dice n times\n",
-        "n_random = 10000\n",
-        "dice_1 = np.random.choice([1,2,3,4,5,6], n_random) \n",
-        "dice_2 = np.random.choice([1,2,3,4,5,6], n_random) \n",
-        "corr = np.corrcoef(x=dice_1, y=dice_2)\n",
-        "print('Corrleation: {}'.format(corr))"
-      ],
+      "execution_count": 71,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -70,35 +49,37 @@
         "id": "W9_dYLQklcRU",
         "outputId": "29129f4b-5e6e-4241-cd50-1af9e57e41bf"
       },
-      "execution_count": 71,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Corrleation: [[1.         0.00711734]\n",
             " [0.00711734 1.        ]]\n"
           ]
         }
+      ],
+      "source": [
+        "# roll the dice n times\n",
+        "n_random = 10000\n",
+        "dice_1 = np.random.choice([1,2,3,4,5,6], n_random) \n",
+        "dice_2 = np.random.choice([1,2,3,4,5,6], n_random) \n",
+        "corr = np.corrcoef(x=dice_1, y=dice_2)\n",
+        "print('Corrleation: {}'.format(corr))"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Apart from small numerical fluctuations, the two set of random numbers are uncorrelated, as we expect."
-      ],
       "metadata": {
         "id": "8IHN-3yVnYvY"
-      }
+      },
+      "source": [
+        "Apart from small numerical fluctuations, the two set of random numbers are uncorrelated, as we expect."
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "sns.histplot(dice_1, label='dice 1', color='k', bins=6)\n",
-        "sns.histplot(dice_2, label='dice 2', color='r', bins=6)\n",
-        "plt.legend()\n",
-        "plt.show()"
-      ],
+      "execution_count": 72,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -107,32 +88,30 @@
         "id": "NCigEhyjmUZb",
         "outputId": "f0b7ad53-0265-4808-8907-2ba24d75b68f"
       },
-      "execution_count": 72,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": "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\n"
+            ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "sns.histplot(dice_1, label='dice 1', color='k', bins=6)\n",
+        "sns.histplot(dice_2, label='dice 2', color='r', bins=6)\n",
+        "plt.legend()\n",
+        "plt.show()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "sns.histplot(x=dice_1, y=dice_2, bins=6,\n",
-        "             cbar=True, cbar_kws=dict(shrink=.75),\n",
-        "             cmap='rocket')\n",
-        "plt.xlabel('dice 1')\n",
-        "plt.ylabel('dice 2')\n",
-        "plt.show()"
-      ],
+      "execution_count": 73,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -141,35 +120,47 @@
         "id": "ARTbEG5fmxSY",
         "outputId": "a2fc39ca-60d9-4565-83fa-bd3e19dd36c8"
       },
-      "execution_count": 73,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 2 Axes>"
-            ],
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAEGCAYAAABIGw//AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYOElEQVR4nO3de5Cf1X3f8ffHQhgZCJdIJrIkLKaR8RCnFnhN8eBQAr5g4gTccWyYiU0pE3la7MLEmQy4M7XdlE46bXAu0zJVDEFOsDHhUlNKDBhICGm4SELcJIgVDINkgSRjrraB3f30j9+z+GfY32V39exzfno+r5kz+9zPd2fgq7PnOc85sk1ERJTnTU0HEBER00uCjogoVBJ0REShkqAjIgqVBB0RUah9mg6g2+LFi71y5cqmw4iIwm3YsGG37SVNx1G3ohL0ypUrWb9+fdNhREThJD3RdAzzIV0cERGFSoKOiChUEnRERKGSoCMiCpUEHRFRqFoTtKSDJV0t6RFJWyS9r876IiL2JnUPs/tj4Nu2Py5pX+AtNdcXEbHXqC1BSzoIOAH41wC2XwFeqau+iIi9TZ0t6COAXcCfS3o3sAE4z/ZL3RdJWgOsATj88MOHfvhP7vmrPRdpTfxPDzUdQn+HjsCHWC8933QEgy1+W9MR9PfqaLSLFp28pukQilNnH/Q+wDHAJbaPBl4CLnj9RbbX2h6zPbZkyQgkjIiIeVJngt4GbLN9d7V/NZ2EHRERQ6gtQdt+CnhS0pHVoZOBzXXVFxGxt6l7FMfngCuqERyPAWfXXF9ExF6j1gRtexMwVmcdERF7q3xJGBFRqCToiIhCJUFHRBQqCToiolBJ0BERhUqCjogoVBJ0REShkqAjIgqVBB0RUagk6IiIQiVBR0QUKgk6IqJQdc9mFxFRpA+fdIJ3P/PDnuc33v/QTbZPmceQ3iAJOiJaafcPnuGum6/peX7fX3jn4nkMZ1pJ0BHRXpOTTUfQVxJ0RLSTjSfGm46iryToiGgpw8SrTQfRVxJ0RLSXy+7iyDC7iGinqoujVxlE0n6S7pF0v6SHJX25On6EpLslbZX0zWpNViS9udrfWp1fOaiOJOiIaCl3XhL2KoO9DJxk+93AauAUSccB/xX4iu1fBH4InFNdfw7ww+r4V6rr+kqCjoh2ctUH3asMvN22/WK1u7AqBk4Crq6OrwNOr7ZPq/apzp8sSf3qSIKOiPbyZO8CiyWt7yprXn+7pAWSNgE7gVuAfwKetT3VR7INWFZtLwOeBKjOPwf8fL/w8pIwItrJhv59zbttj/V/hCeA1ZIOBq4D3rkHI0wLOiJabG590K+x/SxwO/A+4GBJU43f5cD2ans7sAKgOn8Q8IN+z02Cjoh2svHkqz3LIJKWVC1nJC0CPghsoZOoP15ddhbwrWr7+mqf6vxttt2vjnRxRERLDeziGGQpsE7SAjqN3ats3yBpM3ClpP8M3AdcWl1/KfAXkrYCzwBnDKogCToi2smGyYk53O4HgKOnOf4YcOw0x38C/OZM6kiCjoj2ylwc9fBPXmo6hMEOPKjpCPrbZ9+mIxhonxMG/hXYuMnndjYdQl+T99/WdAiFcvGfeo9sgo6ImBMbxtOCjogojoHOMOZy1ZqgJT0OvABMAOODBn1HRMybwR+qNG4+WtC/anv3PNQTETG8JOiIiIIVvuRV3V8SGrhZ0obpJhoBkLRmajKSXbt21RxORERlqgXdqxSg7hb0+21vl/RW4BZJj9i+o/sC22uBtQBjY2N9P3uMiNhz3O4WtO3t1c+ddGZ6esPXNRERjTDFt6BrS9CS9pd04NQ28CHgobrqi4iYGQ+aD7pxdXZxHAZcVy0YsA/wddvfrrG+iIgZaPEojmrCkHfX9fyIiDkx+ZIwIqJM7ozkKFgSdES0U1rQERGlKn82uyx5FRHtZGBioncZQNIKSbdL2izpYUnnVce/JGm7pE1VObXrngslbZX0qKQPD6ojLeiIaCfP+UOVceDztjdWQ4o3SLqlOvcV2/+9+2JJR9FZ5uqXgLcB35H0DveZUi8JOiJaam7D7GzvAHZU2y9I2gIs63PLacCVtl8GvletTXgs8A+9bkgXR0S0k8HjEz0LsHhqnqCqTDufEICklXTWJ7y7OvRZSQ9IukzSIdWxZcCTXbdto39CT4KOiLYa+CXhbttjXWXtdE+RdABwDXC+7eeBS4B/Bqym08L+w9lGmC6OiGgnA+NzW1FF0kI6yfkK29cC2H666/yfATdUu9uBFV23L6+O9ZQWdES0VPWSsFcZQJ15LC4Ftti+uOv40q7LPsZP5yC6HjhD0pslHQGsAu7pV0da0BHRTlPD7GbveOBTwIOSNlXHvgCcKWl1VcPjwGcAbD8s6SpgM50RIOf2G8EBSdAR0WaTs//U2/adgKY5dWOfey4CLhq2jiToiGgne64t6NolQUdEO9lTw+mKlQQdEe1V+FwcSdAR0U72nIfZ1S0JOiLaaw4vCedDEnREtNPch9nVLgk6IlrKeG6z2dUuCToi2snAeBJ0RER5Mg66Pm86tO8sfUWYfOn5pkPob+G+TUcw0PjmO5oOYbDC/0xm0QFNR1As5yVhRESBMswuIqJgaUFHRJTHBk+U3T2VBB0RLeW0oCMiimRwhtlFRBTIwHha0BERRSp9mF3WJIyIdrI7LeheZQBJKyTdLmmzpIclnVcdP1TSLZK+W/08pDouSX8iaaukByQdM6iO2hO0pAWS7pN0w+CrIyLmjyfdswxhHPi87aOA44BzJR0FXADcansVcGu1D/AROgvFrgLWAJcMqmA+WtDnAVvmoZ6IiKHZ4HH3LIPv9w7bG6vtF+jkuWXAacC66rJ1wOnV9mnA19xxF3Dw61YAf4NaE7Sk5cCvAV+ts56IiBkzMNmnwGJJ67vKml6PkrQSOBq4GzjM9o7q1FPAYdX2MuDJrtu2Vcd6qvsl4R8Bvwcc2OuC6pdeA3D44YfXHE5ERMXg8b5X7LY9Nugxkg4ArgHOt/289NOFvm1b0qzfRNbWgpb0UWCn7Q39rrO91vaY7bElS5bUFU5ExBt4vHcZhqSFdJLzFbavrQ4/PdV1Uf3cWR3fDqzoun15daynOrs4jgd+Q9LjwJXASZL+ssb6IiKG586asb3KIOo0lS8Ftti+uOvU9cBZ1fZZwLe6jn+6Gs1xHPBcV1fItGrr4rB9IXAhgKQTgd+1/Vt11RcRMRM2TA7ZUu7heOBTwIOSNlXHvgD8AXCVpHOAJ4BPVOduBE4FtgI/As4eVEE+VImI9rIGX9PrVvtOoNcDTp7megPnzqSOeUnQtv8G+Jv5qCsiYiiGyfHZJ+j5kBZ0RLTWMH3NTer7klDSOyWdXA0j6T5+Sr1hRUTUyxaTE71LCXomaEn/ns7bx88BD0k6rev0f6k7sIiIupWeoPt1cfw28B7bL1ZfyVwtaaXtP6Z3x3hExGgweLLsVNYvQb/J9osAth+vhspdLentJEFHxIgzFNNS7qVfH/TTklZP7VTJ+qPAYuCX6w4sIqJWVQu6VylBvxb0p+lMp/ca2+N0voT5X7VGFRFRu3L6mnvpmaBtb+tz7u/rCSciYn4YmJzDhyrzIeOgI6KdDJMTZS8qlQQdEe3k0X5J+BpJb5f0gWp7kaSe8ztHRIwCIybdu5RgYIKW9NvA1cDUi8HlwP+uM6iIiPkwOamepQTDtKDPpTOt3vMAtr8LvLXOoCIi6jb1krDkFvQwfdAv235lahkXSfvQ+d0iIkaXKaal3MswCfpvJX0BWCTpg8C/A/5PvWFFRNSvlJZyL8Mk6AuAc4AHgc/QWRWg8VW6J7//3aZDGMj/uLnpEPrS0cc1HcJgz+5uOoLBFu7bdAT9/fjFpiMokhETk2UPsxsmukXAZbZ/0/bHgcuqYxERI8uGCatnGUTSZZJ2Snqo69iXJG2XtKkqp3adu1DSVkmPSvrwMDEOk6Bv5WcT8iLgO8M8PCKiZLZ6liFcDkw3N/5XbK+uyo0Ako4CzgB+qbrnf0paMKiCYRL0flOz2nV+Ib8IvGWI+yIiCiYm+pRBbN8BPDNkZacBV9p+2fb36Cwce+ygm4ZJ0C9JOmZqR9J7gB8PGVRERJE6w+x6F2CxpPVdZc2Qj/6spAeqLpBDqmPLgCe7rtlWHetrmJeE5wN/Jen7dOaB/gXgk0MGGhFRrIn+bdTdtsdm+MhLgN+nk/9/H/hD4N/MLrohErTteyW9EziyOvSo7VdnW2FERAkM7Ok1Y20/PbUt6c+AG6rd7cCKrkuXV8f66pmgJZ1k+zZJ/+p1p94hCdvXDh92RER5hulrnglJS23vqHY/BkyN8Lge+Lqki4G3AauAewY9r18L+l8CtwG/Ps05A0nQETGyzNwStKRvACfS6aveBnwROLFaicrA43S+HcH2w5KuAjbTWQjlXNsTg+roN2H/F6ufZ8/6N4iIKNhcvvS2feY0hy/tc/1FwEUzqaNfF8fv9LvR9sUzqSgioiQecjhdk/p1cUzN+Xwk8F46fSjQ6fIY2HcSEVG6Pf2ScE/r18XxZQBJdwDH2H6h2v8S8H/nJbqIiJoYmNDotqCnHAa80rX/SnUsImKkjWwLusvXgHskXVftn07nG/SIiJFlwfiot6BtXyTpr4FfqQ6dbfu+esOKiKhf6SuPDLWqt+2NwMaZPFjSfsAdwJureq6eGroXEdE0A+NlN6CHS9Cz9DJwku0XJS0E7pT017bvqrHOiIihdF4SNh1Ff7UlaNsGpqYpXViV0v+iiIgWKf0lYa3rvUhaIGkTsBO4xfbd01yzZmo6v127dtUZTkTEa6Za0L1KCWpN0LYnbK+mM3PTsZLeNc01a22P2R5bsmRJneFERPyMyT6lBPOyYqLtZ4HbmX55mIiIedeZLKl3KUFtCVrSEkkHV9uLgA8Cj9RVX0TETE2qdylBnaM4lgLrqoUR3wRcZfuGAfdERMwL05n3s2R1juJ4ADi6rudHRMyFC3oZ2EudLeiIiKKV8jKwl3l5SRgRUZrOS0L3LINUq3bvlPRQ17FDJd0i6bvVz0Oq45L0J5K2Vit+HzNMjEnQEdFacxxmdzlvHJl2AXCr7VXArdU+wEforEO4ClhDZ/XvgZKgI6KV5jrMzvYdwDOvO3wasK7aXkdn9s+p419zx13AwZKWDqojfdAR0VoDhtMtlrS+a3+t7bUDHnlY16reT/HTufOXAU92XbetOraDPpKgI6KVpvqg+9hte2zWz7ctaU7zDyVBR0QrGTO+5+dve1rSUts7qi6MndXx7cCKruuWV8f6Sh90RLSW+5RZuh44q9o+C/hW1/FPV6M5jgOe6+oK6WlkW9AL3nFs0yEMNPH8698fFGb795qOYLAlA9+jNG9B4f8b/eDppiMoUudLwtmnYknfAE6k01e9Dfgi8AfAVZLOAZ4APlFdfiNwKrAV+BFw9jB1FP5fVkREfebSwWH7zB6nTp7mWgPnzrSOJOiIaK1hPkhpUhJ0RLSSKf9T7yToiGittKAjIgo015eE8yEJOiJayjgJOiKiPEN8Sdi4JOiIaK28JIyIKJCBCacFHRFRpMl0cURElMdDrpzSpCToiGilzkvCsnuhk6AjorXKTs9J0BHRUjZMuOwUnQQdEa1VdnpOgo6Iluq8JCw7RSdBR0RrOeOgIyLKk0+9IyKK5Tm/JJT0OPACMAGM2x6TdCjwTWAl8DjwCds/nM3zs2hsRLRSZ8J+9ywz8Ku2V9seq/YvAG61vQq4tdqflSToiGitCU/2LHNwGrCu2l4HnD7bB9WWoCWtkHS7pM2SHpZ0Xl11RUTMhvsUOqt1r+8qa3o84mZJG7rOH2Z7R7X9FHDYbOOrsw96HPi87Y2SDgQ2SLrF9uYa64yIGIox4/2H2e3u6rbo5f22t0t6K3CLpEd+pg7bkmb9JrK2FrTtHbY3VtsvAFuAZXXVFxExU7Z7liHv31793AlcBxwLPC1pKUD1c+ds45uXPmhJK4GjgbunObdm6k+IXbt2zUc4ERGvTZbUqwwiaf+qdwBJ+wMfAh4CrgfOqi47C/jWbGOsfZidpAOAa4DzbT//+vO21wJrAcbGxsoelBgRew17zsPsDgOukwSdXPp129+WdC9wlaRzgCeAT8y2gloTtKSFdJLzFbavrbOuiIiZmsuXhLYfA949zfEfACfPIazX1Jag1fln5VJgi+2L66onImK2Sp+Lo84+6OOBTwEnSdpUlVNrrC8iYmgGJu2epQS1taBt3wmorudHRMzN3D/1rlvm4oiIVup8kFJGS7mXkU3QE/d9p+kQBtKqY5oOob/CWw8AXn9b0yEMpHf9i6ZD6MuHLGk6hGKlBR0RUaA9MMyudknQEdFaToKOiCiP85IwIqJcM5z3ed4lQUdEKxmYmEwLOiKiSBlmFxFRooziiIgoU7o4IiIKNpfZ7OZDEnREtFKG2UVElMoUM2tdL/Oy5FVERGlMZy6OXmUYkk6R9KikrZIu2NMxJkFHREv1XjB2mL5pSQuA/wF8BDgKOFPSUXsywiToiGitSU/2LEM4Fthq+zHbrwBXAqftyfiSoCOilfbAiirLgCe79rdVx/aYvCSMiHbyqzdNvPr9xX2u2E/S+q79tbbX1h1WtyToiGgl26fM8RHbgRVd+8urY3tMujgiImbnXmCVpCMk7QucAVy/JytICzoiYhZsj0v6LHATsAC4zPbDe7KOJOiIiFmyfSNwY13PTxdHREShkqAjIgqVBB0RUagk6IiIQiVBR0QUKgk6IqJQSdAREYVKgo6IKFQSdEREoWpL0JIuk7RT0kN11RERsTerswV9OTDX2aIiIlqrtgRt+w7gmbqeHxGxt2t8siRJa4A1AIcffvjQ9y362B5fnzFKtPqjTUcQ0ZjGXxLaXmt7zPbYkiVLmg4nIqIYjSfoiIiYXhJ0RESh6hxm9w3gH4AjJW2TdE5ddUVE7I1qe0lo+8y6nh0R0Qbp4oiIKFQSdEREoZKgIyIKlQQdEVEo2W46htdI2gU8MYNbFgO7awpnPox6/JDfoRSj/jvMNP63297rv2wrKkHPlKT1tseajmO2Rj1+yO9QilH/HUY9/rqkiyMiolBJ0BERhRr1BL226QDmaNTjh/wOpRj132HU46/FSPdBR0TszUa9BR0RsddKgo6IKNRIJuhRX5BW0gpJt0vaLOlhSec1HdNMSdpP0j2S7q9+hy83HdNsSFog6T5JNzQdy2xIelzSg5I2SVrfdDyzIelgSVdLekTSFknvazqmUoxkH7SkE4AXga/ZflfT8cyUpKXAUtsbJR0IbABOt7254dCGJknA/rZflLQQuBM4z/ZdDYc2I5J+BxgDfs72yK2vJelxYMz2yH6kImkd8He2vyppX+Attp9tOq4SjGQLetQXpLW9w/bGavsFYAuwrNmoZsYdL1a7C6syUv/aS1oO/Brw1aZjaStJBwEnAJcC2H4lyfmnRjJB700krQSOBu5uNpKZq7oHNgE7gVtsj9rv8EfA7wGTTQcyBwZulrShWoB51BwB7AL+vOpq+qqk/ZsOqhRJ0A2SdABwDXC+7eebjmembE/YXg0sB46VNDLdTZI+Cuy0vaHpWObo/baPAT4CnFt1/42SfYBjgEtsHw28BFzQbEjlSIJuSNVvew1whe1rm45nLqo/SW8HTmk6lhk4HviNqg/3SuAkSX/ZbEgzZ3t79XMncB1wbLMRzdg2YFvXX19X00nYQRJ0I6oXbJcCW2xf3HQ8syFpiaSDq+1FwAeBR5qNani2L7S93PZK4AzgNtu/1XBYMyJp/+olM1W3wIeAkRrZZPsp4ElJR1aHTgZG5mV53Wpbk7BO1YK0JwKLJW0Dvmj70majmpHjgU8BD1Z9uABfsH1jgzHN1FJgnaQFdP6hv8r2SA5VG2GHAdd1/r1nH+Drtr/dbEiz8jngimoEx2PA2Q3HU4yRHGYXEdEG6eKIiChUEnRERKGSoCMiCpUEHRFRqCToiIhCJUHHvJP0JUm/W23/J0kf2APPHOkZDiOmkwQdjbL9H21/Zw886nJG60vGiIGSoGNeSPoPkv5R0p3AkV3HL5f08Wr7vZL+XzXH9D2SDqwmZPpvku6V9ICkz0z3/FGf4TBiOiP5JWGMFknvofM59Wo6/81tpDMHdvc1+wLfBD5p+15JPwf8GDgHeM72eyW9Gfh7STfb/t68/hIRDUiCjvnwK8B1tn8EIOn6aa45Ethh+16Aqdn9JH0I+OdTrWzgIGAVkAQde70k6CidgM/ZvqnpQCLmW/qgYz7cAZwuaVE1+9qvT3PNo8BSSe8FqPqf9wFuAv5tNT0rkt6RCd2jLdKCjtpVay9+E7ifzuor905zzSuSPgn8aTV96Y+BD9BZjmolsLGapnUXcPrr798LZjiMeIPMZhcRUah0cUREFCoJOiKiUEnQERGFSoKOiChUEnRERKGSoCMiCpUEHRFRqP8PhlcH4li36RMAAAAASUVORK5CYII=\n"
+            ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "sns.histplot(x=dice_1, y=dice_2, bins=6,\n",
+        "             cbar=True, cbar_kws=dict(shrink=.75),\n",
+        "             cmap='rocket')\n",
+        "plt.xlabel('dice 1')\n",
+        "plt.ylabel('dice 2')\n",
+        "plt.show()"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "TVfjYZFd1k1A"
+      },
       "source": [
         "# With Intervention\n",
         "\n",
         "Now we do the same thing but only record the numbers if one of the dice shows either a 5 or a 6."
-      ],
-      "metadata": {
-        "id": "TVfjYZFd1k1A"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 79,
+      "metadata": {
+        "id": "fAlvEKJM17AL"
+      },
+      "outputs": [],
       "source": [
         "dice_1 = []\n",
         "dice_2 = []\n",
@@ -180,34 +171,27 @@
         "  if d_1 == 5 or d_1 == 6 or d_2 == 5 or d_2 == 6:\n",
         "    dice_1.append(d_1[0])\n",
         "    dice_2.append(d_2[0])"
-      ],
-      "metadata": {
-        "id": "fAlvEKJM17AL"
-      },
-      "execution_count": 79,
-      "outputs": []
+      ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
+      "metadata": {
+        "id": "iUXf1azM20LE"
+      },
       "source": [
-        "Now we compute the correlation between the two set of numbers.\n",
+        "Now we compute the correlation between the two sets of numbers.\n",
         "The two numbers are now strongly correlated.\n",
         "\n",
         "This is not surprising, because we changed the way we recorded the data:\n",
         "We only record the outcomes of the two dice if one of them shows a 5 or a 6.\n",
         "Hence, we expect that we observe these two numbers in the final set of recorded values much more frequently than any other value - and we do see this in the plot below.\n",
         "All other numbers occur as well - but we do not observe them as frequently as we see the numbers 5 and 6."
-      ],
-      "metadata": {
-        "id": "iUXf1azM20LE"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "corr = np.corrcoef(x=np.array(dice_1), y=np.array(dice_2))\n",
-        "print('Corrleation: {}'.format(corr))"
-      ],
+      "execution_count": 85,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -215,26 +199,24 @@
         "id": "NPe4CNJw24ub",
         "outputId": "e13af895-2288-4ab2-ec80-8095fd0ea1bb"
       },
-      "execution_count": 85,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Corrleation: [[ 1.         -0.50814839]\n",
             " [-0.50814839  1.        ]]\n"
           ]
         }
+      ],
+      "source": [
+        "corr = np.corrcoef(x=np.array(dice_1), y=np.array(dice_2))\n",
+        "print('Corrleation: {}'.format(corr))"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "sns.histplot(dice_1, label='dice 1', color='k', bins=6)\n",
-        "sns.histplot(dice_2, label='dice 2', color='r', bins=6)\n",
-        "plt.legend()\n",
-        "plt.show()"
-      ],
+      "execution_count": 82,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -243,32 +225,30 @@
         "id": "_Z67o4FL3vrM",
         "outputId": "46fdae80-c597-4c7d-f3a4-2dc503ef4d01"
       },
-      "execution_count": 82,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": "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\n"
+            ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "sns.histplot(dice_1, label='dice 1', color='k', bins=6)\n",
+        "sns.histplot(dice_2, label='dice 2', color='r', bins=6)\n",
+        "plt.legend()\n",
+        "plt.show()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "sns.histplot(x=dice_1, y=dice_2, bins=6,\n",
-        "             cbar=True, cbar_kws=dict(shrink=.75),\n",
-        "             cmap='rocket')\n",
-        "plt.xlabel('dice 1')\n",
-        "plt.ylabel('dice 2')\n",
-        "plt.show()"
-      ],
+      "execution_count": 83,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -277,24 +257,34 @@
         "id": "aFX5kqVb4S6w",
         "outputId": "dfc3a605-8323-429c-87b9-9a353908a7d5"
       },
-      "execution_count": 83,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 2 Axes>"
-            ],
-            "image/png": "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\n"
+            ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "sns.histplot(x=dice_1, y=dice_2, bins=6,\n",
+        "             cbar=True, cbar_kws=dict(shrink=.75),\n",
+        "             cmap='rocket')\n",
+        "plt.xlabel('dice 1')\n",
+        "plt.ylabel('dice 2')\n",
+        "plt.show()"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "9cBsr_lGFAT4"
+      },
       "source": [
         "When we think about this in the sequence: We change the way we record the data, and then we observe a change in the outcome, it seems obvious what is happening and why.\n",
         "\n",
@@ -305,10 +295,21 @@
         "With our knowledge \"behind the scenes\" we know that these effects are just artefacts from the way we obtain the data.\n",
         "\n",
         "This highlights the importance of understanding not only where the data we use comes from, but also how it was recorded and which potential issues may arise from this setup."
-      ],
-      "metadata": {
-        "id": "9cBsr_lGFAT4"
-      }
+      ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/datascienceintro/ExplainableAI_Classification_Adult_EBM.ipynb b/datascienceintro/ExplainableAI_Classification_Adult_EBM.ipynb
index 1b20457215d926c1a607f44b841ad3c8262e71e6..a394a5a9d8a38c8257294bba19bfbe66443faebe 100644
--- a/datascienceintro/ExplainableAI_Classification_Adult_EBM.ipynb
+++ b/datascienceintro/ExplainableAI_Classification_Adult_EBM.ipynb
@@ -15,7 +15,7 @@
         "EBM is based on Generalized Additive Models with Pairwise Interactions (GA2M) by Lou et al. ([Accurate Intelligible Models with Pairwise Interactions](https://www.cs.cornell.edu/~yinlou/papers/lou-kdd13.pdf))\n",
         "\n",
         "\n",
-        "We will use the [adult](https://archive.ics.uci.edu/ml/datasets/adult) that focuses on a (binary) classification task whether or not a person makes more than 50k USD per year. The data are taken from a 1994 census and were first discussed in the paper [Ron Kohavi, \"Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid\", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996](https://www.academia.edu/download/40088603/Scaling_Up_the_Accuracy_of_Naive-Bayes_C20151116-5477-1fw84ob.pdf)\n",
+        "We will use the [adult dataset](https://archive.ics.uci.edu/ml/datasets/adult) that focuses on a (binary) classification task whether or not a person makes more than 50k USD per year. The data are taken from a 1994 census and were first discussed in the paper [Ron Kohavi, \"Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid\", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996](https://www.academia.edu/download/40088603/Scaling_Up_the_Accuracy_of_Naive-Bayes_C20151116-5477-1fw84ob.pdf)\n",
         "\n",
         "The data have a number of categorial and numerical features.\n",
         "We can access the data directly from the archive (or use a local copy).\n",
@@ -348,7 +348,7 @@
         "\n",
         "As this is a \"white box\" model, we can look at the details of individual predictions.\n",
         "\n",
-        "In the interactive widget, we can select individual cases and then analyse how the features contribute to the classification result\n"
+        "In the interactive widget, we can select individual cases and then analyse how the features contribute to the classification result.\n"
       ]
     },
     {
diff --git a/datascienceintro/ExplainableAI_Regression_WineQuality_SHAP.ipynb b/datascienceintro/ExplainableAI_Regression_WineQuality_SHAP.ipynb
index caa5fe85d3c1f01e48cae9998a60684066ac69e7..aecb9db8d2b335633107ccdba3125a92095baf82 100644
--- a/datascienceintro/ExplainableAI_Regression_WineQuality_SHAP.ipynb
+++ b/datascienceintro/ExplainableAI_Regression_WineQuality_SHAP.ipynb
@@ -297,7 +297,7 @@
     "These scatter plots allow us to explore how the a single feature contributes to the predictions in more detail. Each point in the scatter-plot is a single prediction.\n",
     "\n",
     "The x-axis show the numerical value of the feature we want to test (e.g. alcohol content in this example), the y-axis the corresponding Shapley value for this feature.\n",
-    "In this example we can see that the importance raises with increasing levels of alcohol "
+    "In this example we can see that the importance raises with increasing levels of alcohol. "
    ]
   },
   {
@@ -450,7 +450,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Then, the individual features are shown with an indication how much they influence the deviatoin from the base value for in this event. Features in red indicate that they push the value up, features in blue push the value down.  The relative size indicates by how much."
+    "Then, the individual features are shown with an indication how much they influence the deviation from the base value for this event. Features in red indicate that they push the value up, features in blue push the value down.  The relative size indicates by how much."
    ]
   },
   {
diff --git a/datascienceintro/PyTorch_ExplainableAI_ImageLime.ipynb b/datascienceintro/PyTorch_ExplainableAI_ImageLime.ipynb
index 9a5ec38db97058c54c6fd4c37b549430a9a47906..c96fd099fffb3764c3d7d8a071c7fefde1159215 100644
--- a/datascienceintro/PyTorch_ExplainableAI_ImageLime.ipynb
+++ b/datascienceintro/PyTorch_ExplainableAI_ImageLime.ipynb
@@ -290,6 +290,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "kR7zroCibQL7"
@@ -300,7 +301,7 @@
         "- convert the image to a tensor \n",
         "- normalise it according to the ImageNet receipe.\n",
         "\n",
-        "[Inception V3](https://pytorch.org/hub/pytorch_vision_inception_v3/) expects the size of the images to be 299x299 pixels, as well as normalised to ```mean = [0.485, 0.456, 0.406]``` and ```std = [0.229, 0.224, 0.225]```.\n",
+        "[Inception v3](https://pytorch.org/hub/pytorch_vision_inception_v3/) expects the size of the images to be 299x299 pixels, as well as normalised to ```mean = [0.485, 0.456, 0.406]``` and ```std = [0.229, 0.224, 0.225]```.\n",
         "\n",
         "Because we use the LIME package later, we split this into two aspects: The general resizing and cropping that can also be applied to the NumPy-style presentation, and then the part that is specific to PyTorch and the model."
       ]
diff --git a/datascienceintro/PyTorch_FineTune.ipynb b/datascienceintro/PyTorch_FineTune.ipynb
index 27990b535be65ecc028f273b5e57c8d6faa4c270..52a9c59d097628dd39e63761d9eb47a798d32aa7 100644
--- a/datascienceintro/PyTorch_FineTune.ipynb
+++ b/datascienceintro/PyTorch_FineTune.ipynb
@@ -1,22 +1,11 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
-  },
   "cells": [
     {
+      "attachments": {},
       "cell_type": "markdown",
+      "metadata": {
+        "id": "ApRh9azeXlD7"
+      },
       "source": [
         "# Finetuning in PyTorch\n",
         "\n",
@@ -32,15 +21,12 @@
         "- Learning-rate scheduler\n",
         "- data augmentation\n",
         "\n",
-        "Previously, we have used a fixed learning rate for the whole length of the training. However, it may be beneficial to asjust the learning rate as the training progresses. For example, we may want to start with a large learning rate to begin with, and then decrease it as we come nearer to the (local) optimum of the training. The [Optimizer](https://pytorch.org/docs/stable/optim.html) provides a range of options. For example, we can decrease the learning rate every $n$ steps with the [StepLR](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.StepLR.html#torch.optim.lr_scheduler.StepLR),\n",
+        "Previously, we have used a fixed learning rate for the whole length of the training. However, it may be beneficial to adjust the learning rate as the training progresses. For example, we may want to start with a large learning rate to begin with, and then decrease it as we come nearer to the (local) optimum of the training. The [Optimizer](https://pytorch.org/docs/stable/optim.html) provides a range of options. For example, we can decrease the learning rate every $n$ steps with the [StepLR](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.StepLR.html#torch.optim.lr_scheduler.StepLR),\n",
         "[exponentially](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ExponentialLR.html#torch.optim.lr_scheduler.ExponentialLR), or others.\n",
         "\n",
         "Additionally, we use data augmentation to \"boost\" the effective number of images that are available to us for training. This allows the network to train on variations of the images without having to create images for every conceivable variation. For example, we can crop images so that the ratio of the main object w.r.t. the rest is different, we can rotate or blur images, introduce noise, change the colour or contrast. Applying some augmentations helps us to avoid overtraining and improves the generalisation ability of the network as we increase the variety of image presentations the network sees.\n",
         "Which augmentations to choose depends on the task at hand, and is part of the model development. The library TorchVison provides a range of [image augmentations](https://pytorch.org/vision/stable/transforms.html) that we will use."
-      ],
-      "metadata": {
-        "id": "ApRh9azeXlD7"
-      }
+      ]
     },
     {
       "cell_type": "code",
@@ -70,22 +56,18 @@
     },
     {
       "cell_type": "code",
-      "source": [
-        "!wget https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
-        "!unzip hymenoptera_data.zip"
-      ],
+      "execution_count": 4,
       "metadata": {
-        "id": "arYRlLBGjDlw",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "arYRlLBGjDlw",
         "outputId": "a3e94ec6-d9dc-4d06-c1b7-b3ed15a2cdb3"
       },
-      "execution_count": 4,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "--2023-01-17 08:11:49--  https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
             "Resolving download.pytorch.org (download.pytorch.org)... 13.33.88.59, 13.33.88.36, 13.33.88.85, ...\n",
@@ -506,10 +488,18 @@
             "  inflating: hymenoptera_data/val/bees/abeja.jpg  \n"
           ]
         }
+      ],
+      "source": [
+        "!wget https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
+        "!unzip hymenoptera_data.zip"
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
+      "metadata": {
+        "id": "OcmpGmmNdWgV"
+      },
       "source": [
         "We now define the image transformations and augmentations, and load the data.\n",
         "\n",
@@ -519,7 +509,7 @@
         "\n",
         "We can use the [Compose](https://pytorch.org/vision/stable/generated/torchvision.transforms.Compose.html#torchvision.transforms.Compose) function to \"chain\" several steps together.\n",
         "\n",
-        "We will use the pre-trained [Resnet-18](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet18.html#torchvision.models.resnet18) model.\n",
+        "We will use the pre-trained [ResNet-18](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet18.html#torchvision.models.resnet18) model.\n",
         "This model was trained on the [ImageNet](https://www.image-net.org) data, which is a large database containing millions of images. For training of this model, the images were prepared in the following way:\n",
         "- resizing to ```256x256 pixels```\n",
         "- cropping aroud the centre with ```254x254 pixels```\n",
@@ -527,13 +517,27 @@
         "- normalising the values with ```mean=[0.485, 0.456, 0.406]``` and ```nd std=[0.229, 0.224, 0.225]```\n",
         "\n",
         "Therefore, we also need to process our new images that we want to finetune on using this receipe. For the trainig data, we use ```RandomSizedCrop``` for a random part of the image followed by a flip and then the normalisation is applied, for the test data, we use the images at they are but bring them into the same format as the original training data according to the above receipe."
-      ],
-      "metadata": {
-        "id": "OcmpGmmNdWgV"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "L0dnunJRjK8Q",
+        "outputId": "77364ce8-fdd9-4c18-8be2-c848dd2bfae7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['ants', 'bees']\n"
+          ]
+        }
+      ],
       "source": [
         "##\n",
         "## load images and transform data\n",
@@ -571,65 +575,24 @@
         "##\n",
         "class_names = train_data.classes\n",
         "print(class_names)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "L0dnunJRjK8Q",
-        "outputId": "77364ce8-fdd9-4c18-8be2-c848dd2bfae7"
-      },
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "['ants', 'bees']\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "MV4QPfxTydmC"
+      },
       "source": [
         "We can now look at an example image.\n",
         "Later on, we will use the data loaders to iterate over the images, therfore, we set the same loop up but ```break``` out after one image.\n",
         "\n",
         "To do so, we first need to transform the image back from the tensor format into the format that we can use to display the image with standard python tools, i.e.\n",
         "[imshow](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html). PyTorch does provide a convenience function for this [ToPILImage](https://pytorch.org/vision/stable/generated/torchvision.transforms.ToPILImage.html). However, we cannot use this here as we need to \"undo\" the normalisation of the colour values to see the original image.\n"
-      ],
-      "metadata": {
-        "id": "MV4QPfxTydmC"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "#\n",
-        "# show an example image\n",
-        "#\n",
-        "\n",
-        "for X,y in test_loader:\n",
-        "    print(\"Shape of X [N images per batch, # colours, height, width]: \", X.shape)\n",
-        "    print(\"Shape of y: \", y.shape, y.dtype)\n",
-        "\n",
-        "    index = 0\n",
-        "    img = X[index]\n",
-        "    print('Shape of image in tensor format {}'.format(img.shape))\n",
-        "    # does the same as ToPILImage but we need to operate on the data\n",
-        "    img = img.numpy().transpose((1, 2, 0))\n",
-        "    print('Shape of image in normal format {}'.format(img.shape))\n",
-        "\n",
-        "    # undo normalisation\n",
-        "    mean = np.array([0.485, 0.456, 0.406])\n",
-        "    std = np.array([0.229, 0.224, 0.225])\n",
-        "    img = std * img + mean\n",
-        "    img = np.clip(img, 0, 1)\n",
-        "    plt.imshow(img)\n",
-        "    print('True label: {}'.format(class_names[y[index]]) )\n",
-        "    break"
-      ],
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -638,11 +601,10 @@
         "id": "LbinKztY8rL8",
         "outputId": "cb8ba907-a7e7-48b2-83d8-b5f11816f378"
       },
-      "execution_count": 7,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Shape of X [N images per batch, # colours, height, width]:  torch.Size([4, 3, 224, 224])\n",
             "Shape of y:  torch.Size([4]) torch.int64\n",
@@ -652,25 +614,54 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
+            ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "#\n",
+        "# show an example image\n",
+        "#\n",
+        "\n",
+        "for X,y in test_loader:\n",
+        "    print(\"Shape of X [N images per batch, # colours, height, width]: \", X.shape)\n",
+        "    print(\"Shape of y: \", y.shape, y.dtype)\n",
+        "\n",
+        "    index = 0\n",
+        "    img = X[index]\n",
+        "    print('Shape of image in tensor format {}'.format(img.shape))\n",
+        "    # does the same as ToPILImage but we need to operate on the data\n",
+        "    img = img.numpy().transpose((1, 2, 0))\n",
+        "    print('Shape of image in normal format {}'.format(img.shape))\n",
+        "\n",
+        "    # undo normalisation\n",
+        "    mean = np.array([0.485, 0.456, 0.406])\n",
+        "    std = np.array([0.229, 0.224, 0.225])\n",
+        "    img = std * img + mean\n",
+        "    img = np.clip(img, 0, 1)\n",
+        "    plt.imshow(img)\n",
+        "    print('True label: {}'.format(class_names[y[index]]) )\n",
+        "    break"
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
+      "metadata": {
+        "id": "yb_jE0IJzg2b"
+      },
       "source": [
         "# Load pretrained model\n",
         "\n",
-        "In this example, we want to start from the pre-trained [Resnet-18](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet18.html#torchvision.models.resnet18) model that was trained using ImageNet data.\n",
+        "In this example, we want to start from the pre-trained [ResNet-18](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet18.html#torchvision.models.resnet18) model that was trained using ImageNet data.\n",
         "\n",
         "The model and its weights are available through PyTorch. We initialise the model with the default values, i.e. the model trained on the ImageNet data.\n",
         "We also need to modify the last layer of the model. In our case, we only have two classes we want to distinguish (bees/ants), so we need to replace the final layer that does the classification with with a new layer that only has two classes.\n",
@@ -681,13 +672,29 @@
         "Rather than hard-coding ```2```, we use ```len(class_names)```.\n",
         "\n",
         "Finally, we must remember to move the model to the GPU (if we have one)\n"
-      ],
-      "metadata": {
-        "id": "yb_jE0IJzg2b"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 63,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IAc1npYClBmf",
+        "outputId": "37f00178-f839-4d3b-a3a9-36ecb8572626"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Using cuda:0 device\n",
+            "Number of ingoing features 512 and classification nodes 1000 in original fully connected layer\n",
+            "Number of ingoing features 512 and classification nodes 2 in new fully connected layer\n"
+          ]
+        }
+      ],
       "source": [
         "##\n",
         "## Load pre-trained model \n",
@@ -711,29 +718,13 @@
         "\n",
         "# move to GPU\n",
         "model = model.to(device)\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "IAc1npYClBmf",
-        "outputId": "37f00178-f839-4d3b-a3a9-36ecb8572626"
-      },
-      "execution_count": 63,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Using cuda:0 device\n",
-            "Number of ingoing features 512 and classification nodes 1000 in original fully connected layer\n",
-            "Number of ingoing features 512 and classification nodes 2 in new fully connected layer\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "LCKL1IjT4yxd"
+      },
       "source": [
         "# Setup training\n",
         "\n",
@@ -741,13 +732,15 @@
         "We will use the [StepLR](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.StepLR.html#torch.optim.lr_scheduler.StepLR) learn-rate scheduler here.\n",
         "\n",
         "We also need to define the loss function, i.e. the metric that we will use to determine how close the predictions are to the true value. Since we work on a classification problem, we will use the [cross-entropy](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html).\n"
-      ],
-      "metadata": {
-        "id": "LCKL1IjT4yxd"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 64,
+      "metadata": {
+        "id": "-tG1yX8e5TiB"
+      },
+      "outputs": [],
       "source": [
         "# Loss function, optimizier, learning rate scheduler\n",
         "loss_func = nn.CrossEntropyLoss()\n",
@@ -758,15 +751,15 @@
         "# Learning rate scheduler\n",
         "lr_schedule = torch.optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n",
         "\n"
-      ],
-      "metadata": {
-        "id": "-tG1yX8e5TiB"
-      },
-      "execution_count": 64,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 65,
+      "metadata": {
+        "id": "yO0Utxu567lO"
+      },
+      "outputs": [],
       "source": [
         "#\n",
         "# Training loop\n",
@@ -836,30 +829,11 @@
         "    correct /= size\n",
         "    print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\") \n",
         "    return correct"
-      ],
-      "metadata": {
-        "id": "yO0Utxu567lO"
-      },
-      "execution_count": 65,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "epochs = 15\n",
-        "loss_values = []\n",
-        "accuracy_values = []\n",
-        "\n",
-        "for t in range(epochs):\n",
-        "\n",
-        "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
-        "    loss = train_epoch(train_loader, model, loss_func, optimizer, lr_schedule)\n",
-        "    loss_values.append(loss)\n",
-        "\n",
-        "    accuracy = test(test_loader, model, loss_func)\n",
-        "    accuracy_values.append(accuracy)\n",
-        "print(\"Done!\")"
-      ],
+      "execution_count": 66,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -867,11 +841,10 @@
         "id": "o3osJ09Y9AKH",
         "outputId": "454bbfb8-c71f-4d26-93cd-2ccc8adb1b43"
       },
-      "execution_count": 66,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Epoch 1\n",
             "-------------------------------\n",
@@ -966,18 +939,26 @@
             "Done!\n"
           ]
         }
+      ],
+      "source": [
+        "epochs = 15\n",
+        "loss_values = []\n",
+        "accuracy_values = []\n",
+        "\n",
+        "for t in range(epochs):\n",
+        "\n",
+        "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
+        "    loss = train_epoch(train_loader, model, loss_func, optimizer, lr_schedule)\n",
+        "    loss_values.append(loss)\n",
+        "\n",
+        "    accuracy = test(test_loader, model, loss_func)\n",
+        "    accuracy_values.append(accuracy)\n",
+        "print(\"Done!\")"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "x = range(0, epochs)\n",
-        "plt.plot(x, loss_values, label='training error')\n",
-        "plt.title('Network training error')\n",
-        "plt.xlabel('Epochs')\n",
-        "plt.ylabel('Loss Function')\n",
-        "plt.show()"
-      ],
+      "execution_count": 67,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -986,24 +967,36 @@
         "id": "wsRPnLjB4m23",
         "outputId": "d83b03ac-dc8e-472e-90c3-705a6b03f5fb"
       },
-      "execution_count": 67,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
+            ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "x = range(0, epochs)\n",
+        "plt.plot(x, loss_values, label='training error')\n",
+        "plt.title('Network training error')\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('Loss Function')\n",
+        "plt.show()"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 71,
+      "metadata": {
+        "id": "HIXO1uJclbWj"
+      },
+      "outputs": [],
       "source": [
         "##\n",
         "## show some images\n",
@@ -1044,18 +1037,11 @@
         "\n",
         "        if images_so_far == n_image: \n",
         "          return\n"
-      ],
-      "metadata": {
-        "id": "HIXO1uJclbWj"
-      },
-      "execution_count": 71,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "show_prediction(model)"
-      ],
+      "execution_count": 72,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1064,11 +1050,10 @@
         "id": "wsceyAO1ohWQ",
         "outputId": "b7521b7e-cfe7-408a-e5ed-52093a868e98"
       },
-      "execution_count": 72,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "True label: bees\n",
             "True label: bees\n",
@@ -1079,18 +1064,36 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 6 Axes>"
-            ],
-            "image/png": 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\n"
+            ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "show_prediction(model)"
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/datascienceintro/PyTorch_GraphNN_MoleculePrediction.ipynb b/datascienceintro/PyTorch_GraphNN_MoleculePrediction.ipynb
index 7ad78583d9f54dbba11fb2512a36c0ffa1601a27..d50a6f925f7bb9f8ad49ee791694f9ecc735a109 100644
--- a/datascienceintro/PyTorch_GraphNN_MoleculePrediction.ipynb
+++ b/datascienceintro/PyTorch_GraphNN_MoleculePrediction.ipynb
@@ -1,6 +1,7 @@
 {
   "cells": [
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "IC7Wb2UPfP7q"
@@ -8,7 +9,7 @@
       "source": [
         "# Graph neural networks in PyTorch\n",
         "\n",
-        "Graph neural networks have emerged as a powerful approach to bring machine learning to systems that can be described by graphs, i.e. by nodes that are connected by edges. This can be, for example, a social graph (such as your connections on social media), or, in natural sciences and engineering, a modecule or a crystal. For molecules and crystals, the nodes are represented by atoms (or maybe atom groups) and the edges, i.e. the connections between the nodes (atoms) are the bonds between the atoms. In the end, the macroscopic properties of a molecule or crystal emerge from the complex interplay between the atoms and their bonds. Using graph neural networks, we want to model this. Instead of building a range of feature variables that describe, for example, the position of the atoms and their immediate or far-ranged envioronment, we start by representing the molecule or crystal as a graph and assign node properties (i.e. features of the atom such as, for example, its position in the periodic table, mass, etc.) and edge features (e.g. specific bonds that are formed).\n",
+        "Graph neural networks have emerged as a powerful approach to bring machine learning to systems that can be described by graphs, i.e. by nodes that are connected by edges. This can be, for example, a social graph (such as your connections on social media), or, in natural sciences and engineering, a molecule or a crystal. For molecules and crystals, the nodes are represented by atoms (or maybe atom groups) and the edges, i.e. the connections between the nodes (atoms) are the bonds between the atoms. In the end, the macroscopic properties of a molecule or crystal emerge from the complex interplay between the atoms and their bonds. Using graph neural networks, we want to model this. Instead of building a range of feature variables that describe, for example, the position of the atoms and their immediate or far-ranged environment, we start by representing the molecule or crystal as a graph and assign node properties (i.e. features of the atom such as, for example, its position in the periodic table, mass, etc.) and edge features (e.g. specific bonds that are formed).\n",
         "\n",
         "In this exercise, we focus on the [QM9 dataset](https://www.nature.com/articles/sdata201422) with a more detailed [description of QM9](https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/datasets/qm9.html), as a publicly availble dataset that is already being brought to a form where we can use it for training a graph neural network.\n",
         "The data contain details about 130,000 molecules and there are 19 regression targets for which we can train a GNN.\n",
@@ -19,7 +20,7 @@
         "The task of this exercise is to predict the isotropic polarizability (the molecule's response of its charge in an external electric field). This is a regression task. For simplicity, we will predict this quantity as a single real-valued number.\n",
         "\n",
         "\n",
-        "The main code of this exerercise was taken from the book [Machine Learning with PyTorch and Scikit-Learn](https://sebastianraschka.com/books/)"
+        "The main code of this exerercise was taken from the book [Machine Learning with PyTorch and Scikit-Learn](https://sebastianraschka.com/books/)."
       ]
     },
     {
@@ -43,20 +44,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 2,
       "metadata": {
         "id": "1kINuHRifGPY"
       },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/home/kerzel/.cache/pypoetry/virtualenvs/datascienceintro-eVBNPtpL-py3.10/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-            "  from .autonotebook import tqdm as notebook_tqdm\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "import torch\n",
         "\n",
@@ -123,6 +115,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "grSasyIwSDII"
@@ -132,11 +125,11 @@
         "\n",
         "The base format of the library PyTorch Geometric is the [Data](torch-geometric.readthedocs.io/en/latest/generated/torch_geometric.data.Data.html#torch_geometric.data.Data) object that describes the graph.\n",
         "\n",
-        "In other machinee learning tasks, the data were given, for example, by a table consisting of feature variables and the target/label (for structured data), or an image together with a label for image analysis tasks.\n",
+        "In other machine learning tasks, the data we're given, for example, by a table consisting of feature variables and the target/label (for structured data), or an image together with a label for image analysis tasks.\n",
         "\n",
-        "Now that we consider graphs, we need to store information about the nodes, the edges (and connection between nodes),as well as the target information.\n",
+        "Now that we consider graphs, we need to store information about the nodes, the edges (and connection between nodes), as well as the target information.\n",
         "\n",
-        "The part ```data.x``` describes the note feature matrix describing, for example, the atoms. The dimensions of this matrix is ```[num_nodes, num_node_features]```\n",
+        "The part ```data.x``` describes the node feature matrix describing, for example, the atoms. The dimensions of this matrix is ```[num_nodes, num_node_features]```\n",
         "\n",
         "The target (label) is given by ```data.y```. In graphs, we can now have multiple layers of targets. We can, for example, have targets that describe the whole graph. In our case, this is the isotropic polarizability. In this case, the shape of the target array is ```[1,*]```, where the ```*``` indicates that we can have many targets to train on (19 in this dataset), that are conveniently combined into the same object.\n",
         "In other situation, we would predict properties of the nodes, in this case the shape of the target object is ```[num_nodes, *]```\n",
@@ -250,12 +243,13 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "XMETBVCFkqfS"
       },
       "source": [
-        "Much of the actual work then consists of putting these data for node and edge features together. The details are described in the [QM9](https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/datasets/qm9.html) documentation, in particular, the ```proceess()``` function.\n",
+        "Much of the actual work then consists of putting these data for node and edge features together. The details are described in the [QM9](https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/datasets/qm9.html) documentation, in particular, the ```process()``` function.\n",
         "\n",
         "For example, the edge attributes are one-hot encoded descriptions of the bond type ```bonds = {BT.SINGLE: 0, BT.DOUBLE: 1, BT.TRIPLE: 2, BT.AROMATIC: 3}```, \n",
         "the ```x``` part for the node attributes includes information about the atomic number, the hybridisation of atomic orbitals, etc.\n"
@@ -385,7 +379,7 @@
         "        data.batch, data.x, data.edge_index, data.edge_attr\n",
         "    )\n",
         "\n",
-        "    #now build the network\n",
+        "    # now build the network\n",
         "    # start with the graph convolutional layers\n",
         "    x = F.relu(self.conv_1(x, edge_index, edge_attr))\n",
         "    x = F.relu(self.conv_2(x, edge_index, edge_attr))\n",
@@ -431,6 +425,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "KH0CCLohpJpf"
@@ -440,7 +435,7 @@
         "\n",
         "Now we set the network up as we did in other cases. Once we have defined the graph neural network using PyTorch Geometric, the interface is identical to the standard PyTorch interface.\n",
         "\n",
-        "We create an instance of our network, define an optimizer (and, if we want, a learning rate scheduler), and move the model (and the data) to the training device (i.e. GPU or CPU)"
+        "We create an instance of our network, define an optimiser (and, if we want, a learning rate scheduler), and move the model (and the data) to the training device (i.e. GPU or CPU)."
       ]
     },
     {
@@ -553,7 +548,7 @@
         "  epoch_loss = 0\n",
         "\n",
         "\n",
-        "  #set the network into training mode\n",
+        "  # set the network into training mode\n",
         "  net.train()\n",
         "  for batch in train_loader:\n",
         "    batch.to(device)\n",
@@ -570,7 +565,7 @@
         "\n",
         "\n",
         "  val_loss = 0\n",
-        "  #set the network in evaluation mode\n",
+        "  # set the network in evaluation mode\n",
         "  net.eval()\n",
         "  for batch in validation_loader:\n",
         "    batch.to(device)\n",
diff --git a/datascienceintro/PyTorch_MNIST.ipynb b/datascienceintro/PyTorch_MNIST.ipynb
index 6b913bcd3cc9c409b712f0efea32281228b77738..162835aa24a0568fc58ceaabb01742d794a63f33 100644
--- a/datascienceintro/PyTorch_MNIST.ipynb
+++ b/datascienceintro/PyTorch_MNIST.ipynb
@@ -1,6 +1,7 @@
 {
   "cells": [
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "XerGI0BJSAQH"
@@ -9,7 +10,7 @@
         "# Image classification with PyTorch\n",
         "\n",
         "In this exercise we start to build our first (deep learning) model ot analyse images.\n",
-        "We use the famous MNIST data that was originally developed by Yann LeCun:  \"THE MNIST DATABASE of handwritten digits\". Yann LeCun, Courant Institute, NYU Corinna Cortes, Google Labs, New York Christopher J.C. Burges, Microsoft Research, Redmond.\n",
+        "We use the famous MNIST data that was originally developed by [Yann LeCun:  \"THE MNIST DATABASE of handwritten digits\". Yann LeCun, Courant Institute, NYU Corinna Cortes, Google Labs, New York Christopher J.C. Burges, Microsoft Research, Redmond.](http://yann.lecun.com/exdb/mnist/)\n",
         "\n",
         "The dataset contains about 70.000 images of hand-written digits 0-9, 60.000 of which are typically used for training, the rest for testing.\n",
         "The images are normalised to a size of 28x28 pixels and anti-aliased which lead to the grayscale values that we can see in the data (as opposed a pure b/w image).\n",
@@ -43,6 +44,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "eGyFnO2taTn5"
@@ -50,7 +52,7 @@
       "source": [
         "# Data access\n",
         "\n",
-        "Now we download the data using the convenience function from TorchVision for the [MNIST](https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html#torchvision.datasets.MNIST) data. It is essentially a wrapper function that downloads the data from Yann LeCun's webpage and add it to the local directory."
+        "Now we download the data using the convenience function from TorchVision for the [MNIST](https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html#torchvision.datasets.MNIST) data. It is essentially a wrapper function that downloads the data from Yann LeCun's webpage and adds it to the local directory."
       ]
     },
     {
@@ -79,6 +81,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "5-6a5r_obCp5"
@@ -88,11 +91,11 @@
         "This can be done using the [DataLoader](https://pytorch.org/docs/stable/data.html) provided by PyTorch. This function provides some common functionality when dealing with the data.\n",
         "In particular, we will need to repeatedly access all images in the dataset and loop over them as the training progresses.\n",
         "Further, experience shows that it is helpful to split the training data into smaller chunks (or batches) to speed up training. This is more efficient than updating the parameters of the network after each single image, or wait until we have processed the entire data in one training loop (or: epoch).\n",
-        "Here we will use a batch size of 64 (images) which works well - however, this is a free parameter and differnt values may work better in other situations.\n",
+        "Here we will use a batch size of 64 (images) which works well - however, this is a free parameter and different values may work better in other situations.\n",
         "\n",
         "After we have defined the DataLoaders for access to the data, we print an example. As we would normally loop over the data, we set the loop up here as well, but terminate it after the first image.\n",
         "\n",
-        "The array/vector/tensor ```X``` contains the images. Therfore, it's dimension reflect the batches of images we process:\n",
+        "The array/vector/tensor ```X``` contains the images. Therefore, it's dimension reflects the batches of images we process:\n",
         "- In each iteration, we train/test the network in batches of 64 images\n",
         "- We have grayscale images (only one colour channel)\n",
         "- the picture is 28x28 pixels high/wide\n",
@@ -158,6 +161,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "ituN_KKZekHR"
@@ -166,7 +170,7 @@
         "# Network definition\n",
         "\n",
         "Before we can train the network, we need to first setup the various components.\n",
-        "In particular, we need to define the architecture of the neural network, as well as the optimizer and other parameters that we use to train the network.\n",
+        "In particular, we need to define the architecture of the neural network, as well as the optimiser and other parameters that we use to train the network.\n",
         "\n",
         "We start by defining the neural network.\n",
         "In PyTorch, we do so by defining a class inheriting vom ```nn.Module```. We need to do two main steps:\n",
@@ -183,7 +187,7 @@
         "- [MaxPool2D](https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html) as the pooling layer\n",
         "\n",
         "When defining the model, we have two hard constraints:\n",
-        "- The input layer needs to be able to handle the 28x28x pixel grayscale images\n",
+        "- The input layer needs to be able to handle the 28x28 pixel grayscale images\n",
         "- We need 10 nodes in the output layer (one for each digit 0-9).\n",
         "How we set the intermediate layers up is, essentially, up to us and this is where the hard work of building and optimising a model comes in.\n",
         "\n",
@@ -287,6 +291,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "Mc3GlViTuv9n"
@@ -294,18 +299,18 @@
       "source": [
         "# Network training\n",
         "\n",
-        "Here we define the functions that run over the data and either train the network or evaluate the test data\n",
+        "Here we define the functions that run over the data and either train the network or evaluate the test data.\n",
         "\n",
-        "The main skelleton is the same:\n",
+        "The main skeleton is the same:\n",
         "- we loop over batches over the data\n",
         "- move the data to the device (CPU or GPU)\n",
-        "- calculate the predictions with the current state of the network (i.e., a forward pass)\n",
+        "- calculate the predictions with the current state of the network (i.e. a forward pass)\n",
         "- determine the quality of the prediction (i.e. the value of the loss function).\n",
         "\n",
         "For the training loop, we then need to tell PyTorch to do the backward propagation and update the network weights.\n",
         "\n",
         "Note:\n",
-        "- We need to tell PyTorch explictly if we train the model, so that the weights can get updated: ```model.train()```. Otherwise, we switch to evaluation mode ```model.eval()```.\n",
+        "- We need to tell PyTorch explicitly if we train the model, so that the weights can get updated: ```model.train()```. Otherwise, we switch to evaluation mode ```model.eval()```.\n",
         "- Our network has 10 output nodes, i.e. each note tells us how likely it is that this image corresponds to the label represented by this node. We need the node with the highest value to identify the most likely classification which we can do with ```argmax```."
       ]
     },
@@ -557,13 +562,6 @@
         "plt.show()"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Hrh_VH-swZgF"
-      },
-      "source": []
-    },
     {
       "cell_type": "markdown",
       "metadata": {
diff --git a/datascienceintro/Pytorch_SimpleLinearRegression.ipynb b/datascienceintro/Pytorch_SimpleLinearRegression.ipynb
index 0fc4237ec70df96f4de68ccc7149d6407b519253..b7946a9dbd3c915b5507d83a0aef5ac314d98804 100644
--- a/datascienceintro/Pytorch_SimpleLinearRegression.ipynb
+++ b/datascienceintro/Pytorch_SimpleLinearRegression.ipynb
@@ -1,42 +1,31 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
+      "attachments": {},
       "cell_type": "markdown",
+      "metadata": {
+        "id": "1NKmY1ZZ73g1"
+      },
       "source": [
         "# Introduction to PyTorch\n",
         "\n",
         "Modern deep learning typically builds on one of the major libraries that allow to efficiently build models without having to deal with the underlying infrastructure and computations.\n",
-        "In particular, they free us from the burden of calculating all the mathematical formulae (mostly linear algebra) to train the network, and to so efficiently on modern GPUs.\n",
+        "In particular, they free us from the burden of calculating all the mathematical formulae (mostly linear algebra) to train the network, and to do so efficiently on modern GPUs.\n",
         "\n",
         "Mostly, two libraries are used: [PyTorch](https://pytorch.org/), developed by Meta/Facebook, or [TensorFlow](https://www.tensorflow.org/), developed by Alphabet/Google. As you can see, a lot of AI research is no longer driven by academia, but industry. Each of these libraries have their strenghts and weaknesses. However, the choice will not limit you in what you can do (maybe how easy it is to implement something). We will choose PyTorch in this course as it has emerged in recent years as them most used package in research, both in academia and industry.\n",
         "\n",
         "Before we will use this library to do any deep learning, we will first cover the basics - using the humble linear regression as an example. Of course, this is efficiently provided by [Linear Regression in Scikit-Learn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html), however, it serves as an example to:\n",
         "- see what is going on step by step\n",
         "- remind ourselves that, in the end, these libraries are also powerful optimisation libraries."
-      ],
-      "metadata": {
-        "id": "1NKmY1ZZ73g1"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 2,
       "metadata": {
         "id": "1sga7qkdmqq6"
       },
+      "outputs": [],
       "source": [
         "import numpy as np\n",
         "import matplotlib.pyplot as plt\n",
@@ -44,12 +33,13 @@
         "\n",
         "import torch\n",
         "import torch.optim as optim"
-      ],
-      "execution_count": 2,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "lQ-XLHAX_FzG"
+      },
       "source": [
         "# Generate data\n",
         "\n",
@@ -57,16 +47,15 @@
         "$$\n",
         "y \\sim m*x + b + \\mathcal{N(0,1)}\n",
         "$$"
-      ],
-      "metadata": {
-        "id": "lQ-XLHAX_FzG"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 3,
       "metadata": {
         "id": "GNPAzxpLuha_"
       },
+      "outputs": [],
       "source": [
         "m_true = 3\n",
         "b_true = 1\n",
@@ -76,12 +65,11 @@
         "std_dev = 1\n",
         "x = np.random.rand(n_samples, std_dev)\n",
         "y = m_true * x + b_true + eps * np.random.randn(n_samples, std_dev)"
-      ],
-      "execution_count": 3,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -90,43 +78,44 @@
         "id": "iMtCbHpPvp9L",
         "outputId": "2e3ca50e-21aa-4f1f-dd64-e60713304f73"
       },
-      "source": [
-        "plt.scatter(x,y)\n",
-        "plt.xlabel('x', fontsize=15)\n",
-        "plt.ylabel('y', fontsize=15)\n",
-        "plt.show()"
-      ],
-      "execution_count": 6,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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hoWYuQ6Rlccb9P/jO5XXPUxlMxiYLjIyOc06LOQmRVjQ1W8nMfp/iENOVwCzwJYq/8NcDtwC/C/z7gM8ZcAfwpLt/PuTcbwb+r7u7mV1MMYC92Ew7RZIWtkCtcqinPCupPFupWmUwqbfzm0hazOuMh84faHY2cG3pz0rge8BXKE41/WXFcX8M3OPuvxZwjn8LfB84AJwovfwXwAoAd7/dzD4GfJTizKYZ4JPu/sOotuXzeZ+YmIh1HSKdFjX1dWR0PHB6qjbskSSY2T53zwe+10BwmKOYIP4qcKe7Px1y3FuBL7t77BxEqxQcJA1BN3WI7jU0er7NO/YH7s1gwNOjl7fvYkRoX3B4H7DL3U/UPThlCg6StM+MHeBre55dcOPODRg4C7boHMwNxJpOWj18VP7syblFvBxQols9B0lCVHBoZLbSd7IYGESSNjZZqAkMUNxwp3rv5ri7wIWtj3BHC9skE1RbSaSObbsORW7DWS3OtNSwY47NzGphm2SCqrKK1NHoGoQ4tY2i6iJpYZtkgXoOInVE3exzixYucIs7BKS6SJJ1Cg4iRC88C7qRG3DNJSvY9idvb2oISHWRJOs0rCR9r97Cs8qFboXpGQbMmHNn91NT5M9+U9OziDR8JFmmnoP0vTiVVTeuGZ7vQZRXObdSUVUk69RzkJ5Vvchs7eohdj81VbNgLe6OalFBRD0A6TUKDtKTgoaK7tnz7Pz7lUNHcXdU07ac0k80rCQ9KehXfrXyr/64M4filOcW6RUKDpJ5zZSwjvtr/rnpmdgzhzT9VPqJhpUk05otYR02VBR0XFSV1EpxynOL9AoFB8m0ejOJwm7UW9avqilsV20wN8Da1UMNBR9NP5V+oWElybSw4aHyTbwwPYNTO600aKjomktW1Awd7X5qqu40VpF+pJ6DZFrY8NCAWd1ppXF+5W/esT/wdc1Akn6nnoNkUjkJXZiewareq1yIVq1dRfI0A0n6nYKDZE45CV3uMTjMB4jycNBwm27qmoEkEkzDSpI5QUlop3Y3tKCd1Bq9qWsGkkgwBQfJnDgrkdt5U9cMJJFaCg6SOXHLWeimLpIc5Rwkc5QHEOk8BQfJpJMWv/5P87QlOW2EI5IyDStJplSXywB4dfZEB1sk0p9S7TmY2XIz221mPzGzg2b28YBjzMy+aGaHzexxM3tHmm2Uzoqz8Y6IJC/tnsNrwKfc/VEzeyOwz8y+6+4/qTjmfcC5pT/vBP6+9L/SBxrZMyFuwTwRaVyqPQd3f97dHy09/lfgSaD6v+YrgLu9aA+w1MzOSLOd0jlxVyxXLpQLqq0kIq3pWELazFYCa4C9VW8NA0cqnh+lNoBIj4o7U0nDTyLJ6khC2sxOAe4HPuHuv2jyHJuATQArVqxoY+ukHeIM+UQdU++z2rJTJFmpBwczy1EMDF9z9wcCDikAyyuen1V6bQF33w5sB8jn88FV2KQj4mzQU++YermDuAvlRKQ5ac9WMuAO4El3/3zIYTuBD5dmLV0CHHP351NrpLQszpBPq8NCWignkqy0ew4jwIeAA2ZWLqT/F8AKAHe/HXgIuAw4DBwHrk+5jdKCsclC6PaclUM+rQ4LqWCeSLJSDQ7u/gOoKc9ffYwDf55Oi6SdykNFYSqHfNoxLKTaSiLJUfkMaZugoaIyo5hXGBkdZ2yyoGEhkYxTcJC2iBpOguJ+DLAw8Vy9x7PqJ4lkh2orScvqDSdVKyeeH9m6TsFAJKPUc5CWRQ0nhdF6BJFsU89BajRasyjqRn/akhwvH5+teV3rEUSyTT0HWaCZmkVhN/rhpYPc9EfnKfEs0oUUHPrU2GSBkdFxztn64PwMImhucVrUzKONa4aVeBbpQhpW6kNRpSsaXZxWHoKamZ1jwIw5d4arhqK0HkGk+yg49KGo3kHcxWljkwVu3nmQ6ZnX8wlz7gt6DCLSvTSs1GfqlbeIszit3POoDAxlKpst0hvUc+gjccpbxKlZVG/qatRiOBHpDgoOfSTqpl7ZO6iXI6i3RmHAIstniUgX0LBSH4m6qTcyg6jeGoU51/YaIt1OwaGPRK1HaCSBHJSXqD6fiHQ3DSv1kS3rVy2YwgrF4aS1q4cYGR2fzzGsXT3E7qemQnMO5cfVs5XK59MCN5HuZ94DQwD5fN4nJiY63YyuUF0aY+3qIe7fV4hMMA/mBkKHnRottSEi2WFm+9w9H/iegkN/GxkdjzW7aHjpII9sXZdCi0QkLVHBQcNKfSToV37c6qiqoirSXxQc+kRYyYxTB3OBi9mqqYqqSH/RbKU+EVYyw4zImUegJLNIP1Jw6BNhw0LTx2drqqaO/Nab5heyDZhx5UUqnCfSbzSs1CeiCupVrogemyyw5b7H5heyzbmz48dHyJ/9JgUIkT6inkOfiFNQD+CWbx1kdm7hDLbZOeeWbx1MvI0ikh0KDn3kpMWv/3WftiQXuHYhaEvPqNdFpDelGhzM7E4ze8HMngh5/91mdszM9pf+fDbN9vWqoBLbr86e6GCLRCTr0u45fBW4tM4x33f3C0t/bk2hTT2vka0/lw7mAs8R9rqI9KZUg4O7Pwy8lOZ3SmNbf9684TxyixaW3M4tMm7ecF4ibRORbMribKV3mdljwHPAp909MBNqZpuATQArVqxIsXnJC6tXFLeOUfVxYQvdgha2xdnsR0R6X+q1lcxsJfBtdz8/4L1fB064+ytmdhnwN+5+br1z9lJtpeqVzFCcVXTlRcM1BfKCCuIFfT43YOAwe8IjPysi/SWqtlKmZiu5+y/c/ZXS44eAnJkt63CzUhWWH7h375FYeYOgz8/OOaecvHjBQrd6gWFsssDI6DjnbH2QkdFxxiYLrV2YiHSVTAUHM3uzWXFprpldTLF9L3a2VekKyw+E7a5WfXzUSuhHtq7jC1ddCMDmHftDb/rl3kdhegbn9TpMChAi/SPtqaz3Aj8CVpnZUTO7wcz+zMz+rHTIB4AnSjmHLwJXey/UFG9AWIG7sH2Zq48P+/yZSwdj3/Qbmd0kIr0p7dlKH3T3M9w95+5nufsd7n67u99eev/v3P08d3+7u1/i7j9Ms31ZELaS+ZLfPC3w+LWrh2J9fsv6VbFv+o3MbhKR3pSpYaVe1OjY/cY1wzWF8D73/gt45sXgG/Pup6ZifX7jmuHYN/2o3oeI9IcsTmXtGWF7KACRyeDKQnhlm3fsDzw26IYf9HmILr5XKWyvaZXtFukf6jkkqJ1j90uXhKxcDnk9SNzie1G9DxHpD+o5JChsGKcwPcPI6HhDi8vC0vKNpOsbWeAW1vsQkf6g4JCgsGEciD/EVHYsZCvPYzOzsVdOl79LN30RqUfDSgkKGsap1MgQU1gy+NTBnNYkiEjbKTgkqHLsPsxz0zOxZjSF5QvM0JoEEWk7BYeEbVwzzCNb14UGiLi//MOSxNMhm/BoTYKItEI5h5SETQ+N+uVfnRsIyhds23Uo1vRUEZFGqOeQkqR++cednioi0gj1HCo0Muunmc8l8ctf+y+ISBIUHEqaXc3c7OfK1q4e4p49zwa+Hpemp4pIu2lYqaTZ1cytroKuro1U73URkTSo51ASpyhd0PBRqxVMVQFVRLJIPYeSepVIw/ZCOHUwuLZR3JyBKqCKSBYpOJRsWb+quNdyhdyAzc/6CRs+MqOl2UKabSQiWaTgUKm6iF3F86jtN6unqF550TDbdh2KtYeDKqCKSBYp51CybdchZk8sjA6zJ3x+MVrUXgiVs4Wamb2k2UYikjXqOZTUSwzHHf7R/ssi0gsUHErqJYbjDv9o9pGI9AINK5XE2RozzvBP3K04RUSyTD2HknYlhjX7SER6gXoOFdqRGFatIxHpBX0bHJotsheHZh+JSLdLdVjJzO40sxfM7ImQ983Mvmhmh83scTN7RxLtCFvtrK01RUSK0s45fBW4NOL99wHnlv5sAv4+iUa0Y7ppnK09RUS6VarBwd0fBl6KOOQK4G4v2gMsNbMz2t2OVqebquchIr0ua7OVhoEjFc+Pll6rYWabzGzCzCamphorb91qsTstdBORXpe14BCbu29397y754eG4m+MA61PN9VCNxHpdVkLDgVgecXzs0qvtVWraxpUZltEel3WprLuBD5mZl8H3gkcc/fnk/iiVqabxllNLSLSzVINDmZ2L/BuYJmZHQVuAnIA7n478BBwGXAYOA5cn2b74tJCNxHpdeZevYlB98nn8z4xMdHpZoiIdBUz2+fu+aD3spZzEBGRDFBwEBGRGgoOIiJSQ8FBRERqKDiIiEiNnpitZGZTwM+b/Pgy4F/a2JxuoevuL7ru/hL3us9298ASEz0RHFphZhNhU7l6ma67v+i6+0s7rlvDSiIiUkPBQUREaig4wPZON6BDdN39RdfdX1q+7r7POYiISC31HEREpIaCg4iI1Oib4GBml5rZITM7bGZbA94/ycx2lN7fa2Yr029l+8W47k+a2U/M7HEz+19mdnYn2tlu9a674rgrzczNrOunO8a5ZjP709Lf90Ez+8e025iUGP/OV5jZbjObLP1bv6wT7WwnM7vTzF4wsydC3jcz+2Lp/5PHzewdDX2Bu/f8H2AA+N/AbwJvAB4D3lZ1zH8Ebi89vhrY0el2p3Tda4Elpccf7ZfrLh33RuBhYA+Q73S7U/i7PheYBE4rPf+NTrc7xWvfDny09PhtwDOdbncbrvv3gHcAT4S8fxnwHcCAS4C9jZy/X3oOFwOH3f1n7v4r4OvAFVXHXAHcVXp8H/AeM7MU25iEutft7rvd/Xjp6R6KW7N2uzh/3wD/HbgNeDXNxiUkzjV/BPiSu78M4O4vpNzGpMS5dgd+vfT4VOC5FNuXCHd/GHgp4pArgLu9aA+w1MzOiHv+fgkOw8CRiudHS68FHuPurwHHgNNTaV1y4lx3pRso/tLodnWvu9TFXu7uD6bZsATF+bt+K/BWM3vEzPaY2aWptS5Zca79ZuCa0g6UDwH/KZ2mdVSj//0vkLU9pKVDzOwaIA/8fqfbkjQzWwR8Hriuw01J22KKQ0vvpthDfNjMLnD36Y62Kh0fBL7q7n9lZu8C/sHMznf3E51uWFb1S8+hACyveH5W6bXAY8xsMcWu54uptC45ca4bM/sD4L8BG9z9lym1LUn1rvuNwPnA98zsGYrjsTu7PCkd5+/6KLDT3Wfd/WngpxSDRbeLc+03AN8AcPcfASdTLE7Xy2L99x+mX4LDj4FzzewcM3sDxYTzzqpjdgLXlh5/ABj3Ulani9W9bjNbA3yZYmDolTHoyOt292PuvszdV7r7Soq5lg3u3s0bkcf5Nz5GsdeAmS2jOMz0szQbmZA41/4s8B4AM/ttisFhKtVWpm8n8OHSrKVLgGPu/nzcD/fFsJK7v2ZmHwN2UZzZcKe7HzSzW4EJd98J3EGxq3mYYpLn6s61uD1iXvc24BTgm6X8+7PuvqFjjW6DmNfdU2Je8y7g35nZT4A5YIu7d3vvOO61fwr4H2a2mWJy+rpu//FnZvdSDPbLSrmUm4AcgLvfTjG3chlwGDgOXN/Q+bv8/x8REUlAvwwriYhIAxQcRESkhoKDiIjUUHAQEZEaCg4iIlJDwUFERGooOIiISA0FBxERqaHgINJmZrbUzI6a2d1Vr+80s5+a2ZJOtU0kLgUHkTYrVTm9AfiQmV0BYGbXA5cD11bsnyGSWSqfIZIQM/sysBG4FNgNfNnd/2tnWyUSj4KDSELM7BTgceBMisXPLuqRkujSBzSsJJIQd38F+DZwEnCHAoN0E/UcRBJiZr8L/BA4AJwNnOfu/6ezrRKJR8FBJAFmdjLwKMXNdP4UeAx4stv3ypD+oWElkWT8JfBm4COl2UnXAZeb2XWdbJRIXOo5iLSZmY0ADwMfcvd/rHh9G/AR4Hx3P9qp9onEoeAgIiI1NKwkIiI1FBxERKSGgoOIiNRQcBARkRoKDiIiUkPBQUREaig4iIhIDQUHERGp8f8B8tM9pF0vbHUAAAAASUVORK5CYII=",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
+            ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "plt.scatter(x,y)\n",
+        "plt.xlabel('x', fontsize=15)\n",
+        "plt.ylabel('y', fontsize=15)\n",
+        "plt.show()"
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
+      "metadata": {
+        "id": "tPT0W1UcAq9_"
+      },
       "source": [
         "# Initialise\n",
         "\n",
-        "Befor we initialise Pytorch, we need to find out if we have a CPU or a GPU available. For this simple example, a CPU is sufficient - but if we do have a GPU, we need to \"move\" all data and models to it.\n",
+        "Before we initialise PyTorch, we need to find out if we have a CPU or a GPU available. For this simple example, a CPU is sufficient - but if we do have a GPU, we need to \"move\" all data and models to it.\n",
         "\n",
-        "We do this by checking if CUDA is avaiable (not just installed), i.e. if there are NVidia graphics drivers."
-      ],
-      "metadata": {
-        "id": "tPT0W1UcAq9_"
-      }
+        "We do this by checking if CUDA is available (not just installed), i.e. if there are NVidia graphics drivers."
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -134,25 +123,28 @@
         "id": "7DjDRobRyyGQ",
         "outputId": "7131bd20-fdcd-4397-ba78-85702a88b3d7"
       },
-      "source": [
-        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
-        "print(device)"
-      ],
-      "execution_count": 7,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "cpu\n"
           ]
         }
+      ],
+      "source": [
+        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+        "print(device)"
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
+      "metadata": {
+        "id": "AbCFmE9WBCmg"
+      },
       "source": [
-        "Internally, PyTorch works with [Torch.Tensor](https://pytorch.org/docs/stable/tensors.html) as numeric data types, see also [Pytorch: Tensor](https://pytorch.org/tutorials/beginner/examples_tensor/polynomial_tensor.html).\n",
+        "Internally, PyTorch works with [Torch.Tensor](https://pytorch.org/docs/stable/tensors.html) as numeric data types, see also [PyTorch: Tensor](https://pytorch.org/tutorials/beginner/examples_tensor/polynomial_tensor.html).\n",
         "\n",
         "In a sense, they are quite similar to a multidimensional [NumPy Array](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html). One major difference is that these tensors can be used both on CPU and on GPU. We can also specify the data type.\n",
         "\n",
@@ -164,13 +156,11 @@
         "\n",
         "The latter step is a crucial detail if we want to make use of, for example, a GPU: If we forget to move either the data or the model to the GPU, we cannot utilise it.\n",
         "Note that we can chain the commands to make the code a bit more efficient"
-      ],
-      "metadata": {
-        "id": "AbCFmE9WBCmg"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -178,6 +168,16 @@
         "id": "7Nbr1C4ly_Bx",
         "outputId": "341826b9-7cef-4824-89a5-ab8da3680796"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "torch.Size([100, 1]) torch.Size([100, 1])\n",
+            "torch.FloatTensor torch.FloatTensor\n"
+          ]
+        }
+      ],
       "source": [
         "#\n",
         "# convert the NumPy arrays with the data from above to PyTorch tensors\n",
@@ -187,21 +187,13 @@
         "\n",
         "print(x_tensor.shape,y_tensor.shape)\n",
         "print(x_tensor.type(),y_tensor.type())"
-      ],
-      "execution_count": 8,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "torch.Size([100, 1]) torch.Size([100, 1])\n",
-            "torch.FloatTensor torch.FloatTensor\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "utRIxhg9Cw-I"
+      },
       "source": [
         "In this case where we want to fit a linear regression model, we have two variables that we need to fit: the slope and the intercept of the line.\n",
         "\n",
@@ -217,13 +209,11 @@
         "Finally, we print the tensors. Note that this gives us quite a bit of information: They are indeed of the datatype \"tensor\", hold a single value in array shape and are used during optimisation.\n",
         "\n",
         "We can get the shape of the array also directly."
-      ],
-      "metadata": {
-        "id": "utRIxhg9Cw-I"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 34,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -231,6 +221,16 @@
         "id": "ml6sp73O7IVj",
         "outputId": "038250d8-79cc-42e6-fab1-d845c3c72439"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "tensor([-0.4388], requires_grad=True) tensor([-1.1128], requires_grad=True)\n",
+            "torch.Size([1]) torch.Size([1])\n"
+          ]
+        }
+      ],
       "source": [
         "#\n",
         "# define Torch variables - if a GPU is available, use this for the computation\n",
@@ -241,28 +241,21 @@
         "b = torch.randn(1, requires_grad=True, dtype=torch.float, device=device)\n",
         "print(m, b)\n",
         "print(m.shape, b.shape)"
-      ],
-      "execution_count": 34,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "tensor([-0.4388], requires_grad=True) tensor([-1.1128], requires_grad=True)\n",
-            "torch.Size([1]) torch.Size([1])\n"
-          ]
-        }
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
+      "metadata": {
+        "id": "SHbIRKJZEnHl"
+      },
       "source": [
         "# Model fit\n",
         "\n",
         "Now we need to fit the model, i.e. our straigt line.\n",
         "\n",
         "We need the following ingredients to do so:\n",
-        "- an optimizer with suitable parameters\n",
+        "- an optimiser with suitable parameters\n",
         "- an error metric\n",
         "\n",
         "The optimizer is the algorithm that determines how to change the values of the parameters in our model (here: slope $m$ and intercept $b$) such that the resuling straight line fits \"better\" to the data we want to fit. This is then quantified by a loss (or: error) function.\n",
@@ -277,13 +270,11 @@
         "$$\n",
         "\n",
         "In this simple example, we do everything manually and step through the fitting. Later, when we define deep learning models, much of this will be done by helper functions."
-      ],
-      "metadata": {
-        "id": "SHbIRKJZEnHl"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 35,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -291,6 +282,16 @@
         "id": "w8HB6w8t7wqf",
         "outputId": "5763612c-862f-438f-8b30-064a2bb5a638"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "slope: tensor([3.0210], requires_grad=True) true slope:  3\n",
+            "intercept:  tensor([0.9898], requires_grad=True) true intercept:  1\n"
+          ]
+        }
+      ],
       "source": [
         "#\n",
         "# Fit the model\n",
@@ -299,19 +300,19 @@
         "learn_rate = 1e-1\n",
         "n_epochs = 1000\n",
         "\n",
-        "# define the optimizer used to learn the parameters\n",
+        "# define the optimiser used to learn the parameters\n",
         "# (Gradient descent)\n",
-        "optimizer = optim.SGD([m, b], lr=learn_rate)\n",
+        "optimiser = optim.SGD([m, b], lr=learn_rate)\n",
         "\n",
         "loss_array = []\n",
         "# training loop\n",
         "for epoch in range(n_epochs):\n",
         "\n",
         "  # prediction\n",
-        "  yhat =  m * x_tensor + b\n",
+        "  y_hat =  m * x_tensor + b\n",
         "\n",
         "  # use MSE as loss function\n",
-        "  error = y_tensor - yhat\n",
+        "  error = y_tensor - y_hat\n",
         "  loss = (error ** 2).mean()\n",
         "  loss_array.append(loss.detach().numpy())\n",
         "\n",
@@ -321,45 +322,30 @@
         "  loss.backward()   \n",
         "\n",
         "  # use the gradient to update the values of the parameters\n",
-        "  optimizer.step()\n",
+        "  optimiser.step()\n",
         "\n",
         "  # clears the computations of the gradients\n",
-        "  optimizer.zero_grad()\n",
+        "  optimiser.zero_grad()\n",
         "\n",
         "print('slope:', m, 'true slope: ', m_true)\n",
         "print('intercept: ', b, 'true intercept: ', b_true)"
-      ],
-      "execution_count": 35,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "slope: tensor([3.0210], requires_grad=True) true slope:  3\n",
-            "intercept:  tensor([0.9898], requires_grad=True) true intercept:  1\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "WOiPBccrHxjI"
+      },
       "source": [
         "Next, we look at the values of the loss function during the optimisation.\n",
         "As we can see, starting from the random values above, we make a big jump in the first few iterations, then the progress is much smaller.\n",
         "\n",
         "However, looking at the plot on a log-scale, we can see that after the initial jump in the first iterations, there is a steady decline over the next about 200 iterations, and only after about 300 iterations or so we start to run into a plateau."
-      ],
-      "metadata": {
-        "id": "WOiPBccrHxjI"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "plt.plot(loss_array)\n",
-        "plt.yscale('log')\n",
-        "plt.show()"
-      ],
+      "execution_count": 43,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -368,37 +354,42 @@
         "id": "d7TqgPykG7Fw",
         "outputId": "56a9ee2a-651f-48e4-9b23-7ff331ed6d05"
       },
-      "execution_count": 43,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD4CAYAAADvsV2wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYr0lEQVR4nO3dfXRV9Z3v8ff3PCUkQAATFXkKEMRSaouNTxXt2GtbtNLO1N6p1NWxSst0ps6tXTP3Xr1dszoz6647daYdK1Nr663WmalLp7VV0UtL1Vo7bS0VRtEgAvGBGhASRB4ChDx97x9nHzgEkCRn5+ydfT6vtbKS/TsnZ393Nnzyy2//zm+buyMiIsmXiroAEREpDwW+iEiFUOCLiFQIBb6ISIVQ4IuIVIhM1AW8nfr6em9sbIy6DBGRUWPt2rU73b3heI/FOvAbGxtZs2ZN1GWIiIwaZrblRI9pSEdEpELEMvDNbLGZ3blnz56oSxERSYxYBr67P+Luy+rq6qIuRUQkMWIZ+CIiEj4FvohIhVDgi4hUCAW+iEiFSGTgf+/Xr/Lo89uiLkNEJFYSGfj3rv49P3lhe9RliIjESiIDP5Myuvv6oy5DRCRWEhn4uUyKXgW+iMhREhn4mZTR06dbN4qIFEtk4GfTKQ3piIgMkNjA15COiMjREhr4GtIRERkokYGfSafoUQ9fROQoZQt8M5tlZneZ2QMjva+cAl9E5BglBb6Z3W1m7WbWMqB9kZltNLNWM7sJwN1fcfelpexvsLJpo7dfQzoiIsVK7eHfAywqbjCzNHA7cDkwD1hiZvNK3M+QZNIpenrVwxcRKVZS4Lv7L4FdA5rPA1qDHn03cD/wscG+ppktM7M1Zramo6NjWHVl0yl61MMXETnKSIzhTwFeL9puA6aY2Slm9m1ggZndfKJvdvc73b3Z3ZsbGo574/WTys/SUQ9fRKRYplw7cvc3gc+XY1/5efjq4YuIFBuJHv5WYFrR9tSgbdBKvYl5Jq3F00REBhqJwH8GmGNmM80sB1wNrBjKC5R6E/Oc3mkrInKMUqdl3gc8Dcw1szYzW+ruvcANwCpgA/ADd19feqmDl0ml6Hfo04VbEZHDShrDd/clJ2hfCawc7uua2WJgcVNT07C+P5sxAHr6+kmn0sMtQ0QkUWK5tEKpQzrZVP6wNFNHROSIWAZ+qbLpQg9fQzoiIgWJDPxMOn9YunArInJELAO/1GmZuSDwNTVTROSIWAZ+yWP4wUVbvflKROSIWAZ+qTK6aCsicoxEBn42XQh89fBFRApiGfiljuEfmaWjHr6ISEEsA7/kMfzCLJ1+Bb6ISEEsA79UmaCH392rIR0RkYJEBn5OPXwRkWMkMvAzac3SEREZKJaBH95FWw3piIgUxDLww7poqx6+iMgRsQz8UinwRUSOlcjAz6Q0pCMiMlAiAz+XUQ9fRGSgRAZ+oYevxdNERI6IZeCXPEtHPXwRkWPEMvBLnaWT0+JpIiLHiGXgl+rIRVv18EVEChIZ+OmUYaZbHIqIFEtk4JsZ2VSKbg3piIgclsjAh/zyCurhi4gckdjAz6RTGsMXESmS2MDPplP09GtIR0SkIJaBX+o8fMgP6fT0qocvIlIQy8AvdR4+BD18DemIiBwWy8APQyZtGtIRESmS2MDPpVMa0hERKZLYwM+kjV718EVEDkts4GsMX0TkaAp8EZEKkeDAN62WKSJSJMGBn9LSCiIiRRIb+BktniYicpTEBn4uo8XTRESKxTLww1haIZPSRVsRkWKxDPzwllbQkI6ISEEsAz8M+Vk66uGLiBQkOPBTeqetiEiRxAZ+Rssji4gcJbGBn0un6NaQjojIYYkNfC2eJiJytMQGfjadoq/f6Vfoi4gACQ98gJ5+DeuIiECiA98ANBdfRCSQ4MDPH5qWVxARyUts4GeCwNdMHRGRvMQGfi4Y0unVkI6ICJDgwM+kgou26uGLiAAJDvxsphD46uGLiABkyrUjM6sFvgV0A79w93tHcn/ZVH5Ip1vLK4iIACX28M3sbjNrN7OWAe2LzGyjmbWa2U1B88eBB9z9c8BHS9nvYFRlNaQjIlKs1CGde4BFxQ1mlgZuBy4H5gFLzGweMBV4PXhaX4n7PalcOg3AIfXwRUSAEgPf3X8J7BrQfB7Q6u6vuHs3cD/wMaCNfOi/7X7NbJmZrTGzNR0dHcOurdDD15COiEjeSFy0ncKRnjzkg34K8GPgKjO7A3jkRN/s7ne6e7O7Nzc0NAy7iFwwD/9Q74j/MSEiMiqU7aKtu+8HrivX/go9fA3piIjkjUQPfyswrWh7atA2aGHcxLwqkx/D15COiEjeSAT+M8AcM5tpZjngamDFUF4gjJuY5zIa0hERKVbqtMz7gKeBuWbWZmZL3b0XuAFYBWwAfuDu60svdWiqMrpoKyJSrKQxfHdfcoL2lcDK4b6umS0GFjc1NQ33JYp6+Ap8ERGI6dIKYQzpVCnwRUSOEsvAD8ORaZkKfBERSHDgmxm5TEoXbUVEArEM/DCmZUJ+WOdQj3r4IiIQ08APYwwf8oGvO16JiOTFMvDDUpVJq4cvIhJIeOCrhy8iUhDLwA9rDD+XSXGoRxdtRUQgpoEf5hi+pmWKiOTFMvDDksuktLSCiEgg0YFflUlrHr6ISCDRgV+dTXFQs3RERICYBn5YF21rchkOdveGVJWIyOgWy8AP66JtbVWa/d0a0hERgZgGflhqcxkOHFIPX0QEEh74NVUZ9nf30d/vUZciIhK5RAd+bS5/X9uDevOViEiyA7+mKn9Dr/26cCsiEs/AD2uWTqGHf+CQevgiIrEM/LBm6dTk1MMXESmIZeCHZWwwpHNAUzNFRJId+DVV+SGd/ZqaKSKS7MCvzamHLyJSkOjArwku2naqhy8ikuzAn1CTBWD3ge6IKxERiV6iA39sVYbqbIqdnQp8EZFYBn5Y8/DNjIZxVXTsOxRSZSIio1csAz+sefgADWMV+CIiENPAD1O9Al9EBKiAwG8YV0VHpwJfRCTxgX/quGp27e+mSytmikiFS3zgzzltLACbduyLuBIRkWglPvDfMXk8ABve2BtxJSIi0Up84M+YVENNLs36bQp8EalsiQ/8VMo4t3EST23qwF23OhSRypX4wAe4bN5pbHnzAJt2dEZdiohIZCoi8C+ffzq5dIr7fvf7qEsREYlMLAM/rKUVCurHVvGRsyfzo7VtWjlTRCpWLAM/zKUVCj594Qz2HerlR2vbQntNEZHRJJaBPxIWTJvAe2dM5M5fvkJPX3/U5YiIlF3FBL6Z8YVLZ7N190FWPLct6nJERMquYgIf4NK5p3LW6eO446mX6e/XFE0RqSwVFfj5Xn4Tre2d/OzFHVGXIyJSVhUV+ABXvGsyjafU8K1ftOqNWCJSUSou8NMp48/+YDbPt+3hV607oy5HRKRsKi7wAf5owVQm11Vz+5OtUZciIlI2FRn4uUyKz108i9++sou1W3ZFXY6ISFlUZOADXH3eNE6pzbH8CfXyRaQyVGzg1+QyfO6SWTy1qYPnXt8ddTkiIiOuYgMf4NMXzGBiTZbbHt8UdSkiIiOuogO/tirDZy+exZMbO3i+Tb18EUm2ig58gD+5cAZ1Y7Isf2Jz1KWIiIyosgW+mc0ys7vM7IFy7XMwxlVn+ezCmTy+oZ2WreEsxywiEkeDCnwzu9vM2s2sZUD7IjPbaGatZnbT272Gu7/i7ktLKXakXHtRI+OrM9ymXr6IJNhge/j3AIuKG8wsDdwOXA7MA5aY2Twze5eZPTrg49RQqw7Z+Oos1y+cyWMv7mD9NvXyRSSZBhX47v5LYOA7lM4DWoOeezdwP/Axd3/B3a8c8NE+2ILMbJmZrTGzNR0dHYM+kFJdd9FMxlVl+GfNyxeRhCplDH8K8HrRdlvQdlxmdoqZfRtYYGY3n+h57n6nuze7e3NDQ0MJ5Q1N3Zgs113UyE/Xb2fDG3vLtl8RkXIp20Vbd3/T3T/v7rPd/e/Ltd+huH7hTMZWZfjmz9XLF5HkKSXwtwLTiranBm0lC/sm5oM1oSbHte+bwcqWN9i0Y19Z9y0iMtJKCfxngDlmNtPMcsDVwIowihqJm5gP1mcXzqImm+YbevetiCTMYKdl3gc8Dcw1szYzW+ruvcANwCpgA/ADd18/cqWWx8TaHEsXzmTlC9s1L19EEsXieNcnM1sMLG5qavrc5s3lnxu/t6uHi295knOmT+B7151X9v2LiAyXma119+bjPRbLpRWiHNKB/Lz8P31/fo2dNa9pvXwRSYZYBn4cfOZ9jdSPreIfV23UvW9FJBEU+CdQk8vwhUtns/rVXbr3rYgkQiwDP6ppmQN96vzpnFFXzdfUyxeRBIhl4Ec9hl9QlUnzxcvmsK5tD4+9uCPSWkREShXLwI+Tq86Zysz6Wr7+s03096uXLyKjlwL/JDLpFDdeNoeNO/bx8LpQ3kgsIhKJWAZ+XMbwCxaffQbvPGM8X1u1ia6evqjLEREZllgGflzG8AtSKeN/XfEOtu4+yL/85rWoyxERGZZYBn4cXdRUz6VzG/jmk628tb876nJERIZMgT8EN1/xDvYf6mX5z3UrRBEZfRT4Q3DmaeP45LnT+P5vt/Dazv1RlyMiMiSxDPy4XbQt9qXLziSbTvEPq16KuhQRkSGJZeDH7aJtsVPHV7PsklmsfGE7a7e8FXU5IiKDFsvAj7tll8zi1HFV/N2jL+rNWCIyaijwh6Eml+Gmy89i3eu7eeA/26IuR0RkUBT4w/RHC6bQPGMit/zkJfYc7Im6HBGRk1LgD5OZ8TcffSe7DnRz62O6/62IxF8sAz/Os3SKzZ9SxzXnT+fffruFl7bvjbocEZG3FcvAj/MsnYH+8oNzGVed4SsPr9ea+SISa7EM/NFkYm2O//7huax+dRcPPafVNEUkvhT4Ibj63OksmD6Bv3vkRd7sPBR1OSIix6XAD0E6Zdxy1dl0Hurlf/+/DVGXIyJyXAr8kJx52jj+7P2zefDZrfxiY3vU5YiIHEOBH6IvfKCJ2Q21fPnBFvYf6o26HBGRoyjwQ1SVSfPVq85m6+6D/OOqjVGXIyJylFgG/miZh3885zZO4jPva+Se37zGrzbvjLocEZHDYhn4o2ke/vH8z0VnMbuhlr/64Tp2H9DdsUQkHmIZ+KPdmFyab3xyATs7D/HXD6+PuhwREUCBP2LeNbWOGy+bwyPrtvHQs3pDlohET4E/gj7//tk0z5jIlx98gdb2zqjLEZEKp8AfQZl0in/+1AKqsmn+/N61HOjWVE0RiY4Cf4RNrhvDbVe/h83tnXz5wRYtsCYikVHgl8HFcxr40mVn8uCzW/n+6t9HXY6IVCgFfpnccGkTl85t4G9XrOfXrZqfLyLlp8Avk1TKWL5kAbMbxvL5769l8459UZckIhVGgV9G46qz3PWZZqoyaa675xk69mkpZREpn1gG/mheWuFkpk6s4a5rm9nZeYhr7/4dew7oBugiUh6xDPzRvrTCybx72gS+8+lmWts7ufZ7v6NTK2uKSBnEMvArwfvPbOCbn1rAC1v3cP09z2iOvoiMOAV+hD70ztO59ZPvYc1ru7jmu6u10JqIjCgFfsQ++u4z+NY172X91r388XeeZvuerqhLEpGEUuDHwKL5p3PP9eeybXcXH//Wr2nZmryL1SISPQV+TLxvdj33L7sAB6664zdaYVNEQqfAj5H5U+p45C8W8u5pE7jx35/jKw+30NXTF3VZIpIQCvyYqR9bxb2fPZ+lC2fyL09v4SPL/4N1r++OuiwRSQAFfgxl0yn++sp5fH/p+Rzo7uPjd/yG/7NyA/u69CYtERk+BX6MLZxTz09vvIRPnDOV//sfr/CBrz/FA2vb6OvXEssiMnQK/JirG5Pllk+czUN/fhFTJozhr364jg/d+hQPPbuV3r7+qMsTkVHE4nxDjubmZl+zZk3UZcRGf7/zk5btLH9iMxt37GP6pBquOX86/7V5GpNqc1GXJyIxYGZr3b35uI8p8Eef/n7nZy9u565fvcozr71FLp1i0fzTufLsyVxyZgPV2XTUJYpIRN4u8DPlLkZKl0oZi+ZPZtH8yWzcvo97V29hxbptrFi3jdpcmkvPOpWL59RzUVM9UyfWRF2uiMRE2Xr4ZvaHwEeA8cBd7v6zk32PeviD19PXz9Mvv8lPWt7g8Q3th9fan3FKDQumTWD+lDreNaWOd06pY2yVfs+LJFXJQzpmdjdwJdDu7vOL2hcBtwFp4Lvu/tVBvNZE4GvuvvRkz1XgD4+709reya9ad/L0y2/yfNsetu89skbPlAljaKyvYWZ9LY2n1DJ1Yg2nja/itPHVNIyrIpvWtXyR0SqMwL8E6AT+tRD4ZpYGNgEfBNqAZ4Al5MP/7we8xPXu3h5839eBe939P0+2XwV+eNr3ddGydQ8tW/fySkcnr755gFc7OtnbdeyyzPVjc5xSW8X4MRnqxmQZX51l/JjgozrDmFya6kya6mya6myKMdk0VcHX1dk0uXSKdMrIpCz4nCKdzm8X2swsgp+CSPKFctHWzBqBR4sC/0Lgb9z9w8H2zQDuPjDsC99vwFeBx9z98bfZzzJgGcD06dPfu2XLlkHVJ0Pn7rx1oIdtuw/Svq+LHXsPsWNvFzv2drFrfzd7Dvaw92Bv/nNXD/uO88thuNKHfxkYKTMMwMAAM8OKvwYseEKhPTXgOQTPybcd+1ipQvv1FNILhVVP7H4+ctjKL148rL+2R+qi7RTg9aLtNuD8t3n+XwCXAXVm1uTu3z7ek9z9TuBOyPfwS6hPTsLMmFSbC6Z0nvzuYn39TmdXL129fXT19NHV08/BnsLX+e2unj56+52+/v7gs9Pb5/QWtvv8SHu/09vXT7+D4xT6Hu6OA17UXtgm2O73o9ud/IYP+P4whPWPMKzrZaH9pwjt56P/pqNF2a7euftyYHm59ifhS6eMuposdWSjLkVEhqGUq3NbgWlF21ODtpIl+SbmIiJRKSXwnwHmmNlMM8sBVwMrwigq6TcxFxGJwqAC38zuA54G5ppZm5ktdfde4AZgFbAB+IG7rx+5UkVEpBSDGsN39yUnaF8JrAy1IvJDOsDipqamsF9aRKRixfIdNhrSEREJXywDX0REwqfAFxGpELEMfE3LFBEJX6zXwzezDmC4ayvUAztDLGc00DFXBh1zZRjuMc9w94bjPRDrwC+Fma050XoSSaVjrgw65sowEsccyyEdEREJnwJfRKRCJDnw74y6gAjomCuDjrkyhH7MiR3DFxGRoyW5hy8iIkUU+CIiFSJxgW9mi8xso5m1mtlNUdcTFjObZmZPmtmLZrbezL4YtE8ys8fMbHPweWLQbma2PPg5PG9m50R7BMNnZmkze9bMHg22Z5rZ6uDY/j1Ynhszqwq2W4PHG6Ose7jMbIKZPWBmL5nZBjO7MOnn2cy+FPy7bjGz+8ysOmnn2czuNrN2M2spahvyeTWza4Pnbzaza4dSQ6ICP7ix+u3A5cA8YImZzYu2qtD0An/p7vOAC4AvBMd2E/CEu88Bngi2If8zmBN8LAPuKH/Jofki+SW4C24BbnX3JuAtYGnQvhR4K2i/NXjeaHQb8FN3Pwt4N/ljT+x5NrMpwH8DmoN7ZqfJ318jaef5HmDRgLYhnVczmwR8hfztZM8DvlL4JTEo7p6YD+BCYFXR9s3AzVHXNULH+jDwQWAjMDlomwxsDL7+DrCk6PmHnzeaPsjfSe0J4APAo+Tvl70TyAw85+TvzXBh8HUmeJ5FfQxDPN464NWBdSf5PHPk/tiTgvP2KPDhJJ5noBFoGe55BZYA3ylqP+p5J/tIVA+f499YfUpEtYyY4E/YBcBq4DR3fyN4aDtwWvB1Un4W3wD+B9AfbJ8C7Pb8DXjg6OM6fMzB43uC548mM4EO4HvBMNZ3zayWBJ9nd98KfA34PfAG+fO2lmSf54KhnteSznfSAj/xzGws8CPgRnffW/yY53/lJ2aerZldCbS7+9qoaymjDHAOcIe7LwD2c+TPfCCR53ki8DHyv+zOAGo5dugj8cpxXpMW+CN2Y/U4MLMs+bC/191/HDTvMLPJweOTgfagPQk/i4uAj5rZa8D95Id1bgMmmFnhbm3Fx3X4mIPH64A3y1lwCNqANndfHWw/QP4XQJLP82XAq+7e4e49wI/Jn/skn+eCoZ7Xks530gJ/xG6sHjUzM+AuYIO7/1PRQyuAwpX6a8mP7Rfa/yS42n8BsKfoT8dRwd1vdvep7t5I/lz+3N2vAZ4EPhE8beAxF34WnwieP6p6wu6+HXjdzOYGTf8FeJEEn2fyQzkXmFlN8O+8cMyJPc9FhnpeVwEfMrOJwV9GHwraBifqixgjcFHkCmAT8DLw5ajrCfG4FpL/c+954Lng4wryY5dPAJuBx4FJwfON/Iyll4EXyM+AiPw4Sjj+PwAeDb6eBfwOaAV+CFQF7dXBdmvw+Kyo6x7msb4HWBOc64eAiUk/z8DfAi8BLcC/AVVJO8/AfeSvUfSQ/0tu6XDOK3B9cOytwHVDqUFLK4iIVIikDemIiMgJKPBFRCqEAl9EpEIo8EVEKoQCX0SkQijwRUQqhAJfRKRC/H9uxv5ZvMEd6AAAAABJRU5ErkJggg==\n"
+            ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "plt.plot(loss_array)\n",
+        "plt.yscale('log')\n",
+        "plt.show()"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "M8gizXhjI4Be"
+      },
       "source": [
         "Finally, we compare the fitted model with our original data, as well as the \"true\" slope and intercept we have generated the data from.\n",
         "\n",
         "Note that in order to be able to do this, we first need to \"de-tensorfy\" the variables. Behind the scenes, PyTorch uses a computational graph to do all calculations, if we want to use the variables, e.g. for plotting, we need to [detach](https://pytorch.org/docs/stable/generated/torch.Tensor.detach.html) the variable from this graph. Further, we need to convert this back into numpy format to be able to plot the data.\n",
         "\n",
         "For the plot, we create a number of points on the $x$-axis (```np.linspace```), and then create a straight line: $ y = m*x + b$."
-      ],
-      "metadata": {
-        "id": "M8gizXhjI4Be"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 45,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -407,31 +398,44 @@
         "id": "88uy1kuInL6O",
         "outputId": "4d504e2a-54b9-4585-e8e4-94879d85cc09"
       },
-      "source": [
-        "plt.scatter(x,y)\n",
-        "x_space = np.linspace(np.min(x), np.max(x))\n",
-        "plt.plot(x_space, m.detach().numpy()*x_space + b.detach().numpy(), label='Fit')\n",
-        "plt.plot(x_space, m_true*x_space +b_true, label='Truth')\n",
-        "plt.legend()\n",
-        "plt.xlabel('x', fontsize = 20)\n",
-        "plt.ylabel('y', fontsize = 20)\n",
-        "plt.show()"
-      ],
-      "execution_count": 45,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": 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\n"
+            ]
           },
           "metadata": {
             "needs_background": "light"
-          }
+          },
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "plt.scatter(x,y)\n",
+        "x_space = np.linspace(np.min(x), np.max(x))\n",
+        "plt.plot(x_space, m.detach().numpy()*x_space + b.detach().numpy(), label='Fit')\n",
+        "plt.plot(x_space, m_true*x_space +b_true, label='Truth')\n",
+        "plt.legend()\n",
+        "plt.xlabel('x', fontsize = 20)\n",
+        "plt.ylabel('y', fontsize = 20)\n",
+        "plt.show()"
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/datascienceintro/Regression_Housing.ipynb b/datascienceintro/Regression_Housing.ipynb
index 7466c1ca776e7980c99d366c8be065d53e5db1bc..8e6feb1d97b0f77f5bb3f4182f0ae0572261c591 100644
--- a/datascienceintro/Regression_Housing.ipynb
+++ b/datascienceintro/Regression_Housing.ipynb
@@ -11,7 +11,7 @@
     "\n",
     "The data are about prices for houses in California, USA and the target variable is the natural logarithm of the median house price.\n",
     "\n",
-    "We will use this example to explore how we an utilise hierarchical features and building a model pipeline in Scikit-Learn.\n",
+    "We will use this example to explore how we can utilise hierarchical features and build a model pipeline in Scikit-Learn.\n",
     "\n",
     "The data can be obtained from the web-page above or, as this is a popular training dataset, using the convenience function [fetch_california_housing](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html) provided by scikit-learn. We will use a local copy in this exercise that has been obtained using this function."
    ]
@@ -592,7 +592,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we can see, the houses are located in areas we visually recognise as Californa, with areas such as Los Angeles or San Francisco densely populted and the deseart areas almost empty. The more expensive houses are also located in the popular cities, just as we would expect.\n",
+    "As we can see, the houses are located in areas we visually recognise as Californa, with areas such as Los Angeles or San Francisco densely populated and the deseart areas almost empty. The more expensive houses are also located in the popular cities, just as we would expect.\n",
     "\n",
     "However, we need to think about how we can make use of this information.\n"
    ]
@@ -604,7 +604,7 @@
    "source": [
     "# Machine Learning Model\n",
     "\n",
-    "We start with a base model that contains the numerical features we can use straight away (i.e. without latitude and longitude)\n",
+    "We start with a base model that contains the numerical features we can use straight away (i.e. without latitude and longitude).\n",
     "\n",
     "We follow the typical Scikit-Learn approach of:\n",
     "- create an instance of the model\n",
@@ -654,7 +654,7 @@
    "metadata": {},
    "source": [
     "Then, we obtain the predictions on the test data for the model.\n",
-    "For convenience, we make a copy of the test data and append the predictions, together with the true values,  as the last column to the dataframe"
+    "For convenience, we make a copy of the test data and append the predictions, together with the true values,  as the last column to the dataframe."
    ]
   },
   {
diff --git a/datascienceintro/Regression_WineQuality.ipynb b/datascienceintro/Regression_WineQuality.ipynb
index 3a536c9094810d99b56ba158e708be4c832f6e6a..1b9b98545a875181ba10fc51b14f1fee86b9e0b3 100644
--- a/datascienceintro/Regression_WineQuality.ipynb
+++ b/datascienceintro/Regression_WineQuality.ipynb
@@ -495,7 +495,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we can see, some variables are strongly correlated with each other but only three variables have a high correlation with out target: volatile acidity sulphates and alcohol. We will therefore suspect that these variables are the most important for the model."
+    "As we can see, some variables are strongly correlated with each other but only three variables have a high correlation with out target: volatile acidity, sulphates, and alcohol. We will therefore suspect that these variables are the most important for the model."
    ]
   },
   {
@@ -622,7 +622,7 @@
    "metadata": {},
    "source": [
     "Then, we obtain the predictions on the test data for the model.\n",
-    "For convenience, we make a copy of the test data and append the predictions, together with the true values,  as the last column to the dataframe"
+    "For convenience, we make a copy of the test data and append the predictions, together with the true values,  as the last column to the dataframe."
    ]
   },
   {
diff --git a/datascienceintro/cluster.ipynb b/datascienceintro/cluster.ipynb
index a697615f6e2dde134c5dea6cca247d2489e52fd5..60d546f6c53d3dedd5457803c98fd2ffc6bf3467 100644
--- a/datascienceintro/cluster.ipynb
+++ b/datascienceintro/cluster.ipynb
@@ -97,7 +97,7 @@
    "source": [
     "### Half-Moons\n",
     "\n",
-    "Here we do the same for the half-moons. The parameter ````noise``` controls how far the moons spead out.\n",
+    "Here we do the same for the half-moons. The parameter ```noise``` controls how far the moons spead out.\n",
     "\n"
    ]
   },
@@ -494,7 +494,7 @@
     "Finally, we look at [Gaussian mixture models](https://scikit-learn.org/stable/modules/mixture.html).\n",
     "This algorithm - similar to KMeans - requires that we set the number of clusters we want to find using out external expert knowledge.\n",
     "\n",
-    "Note how we can combine creating the instance of the model (```GMM```) and fitting in one step if we so wish"
+    "Note how we can combine creating the instance of the model (```GMM```) and fitting in one step if we so wish."
    ]
   },
   {