diff --git a/01_the_machine_learning_landscape.ipynb b/01_the_machine_learning_landscape.ipynb
index 15df3da..9517ef9 100644
--- a/01_the_machine_learning_landscape.ipynb
+++ b/01_the_machine_learning_landscape.ipynb
@@ -9,6 +9,17 @@
     "_This is the code used to generate some of the figures in chapter 1._"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/01_the_machine_learning_landscape.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -111,6 +122,21 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# Download the data\n",
+    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
+    "os.makedirs(datapath, exist_ok=True)\n",
+    "for filename in (\"oecd_bli_2015.csv\", \"gdp_per_capita.csv\"):\n",
+    "    print(\"Downloading\", filename)\n",
+    "    url = DOWNLOAD_ROOT + \"datasets/lifesat/\" + filename\n",
+    "    urllib.request.urlretrieve(url, datapath + filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Code example\n",
     "import matplotlib.pyplot as plt\n",
@@ -201,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -253,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -321,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -334,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -360,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -369,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -378,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -403,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -418,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -435,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -447,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -467,7 +493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -476,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -485,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -506,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -543,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -552,7 +578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -561,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -578,7 +604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -610,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -635,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -644,7 +670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -653,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -685,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -697,7 +723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -708,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -719,7 +745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -758,7 +784,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.3"
   },
   "nav_menu": {},
   "toc": {
diff --git a/02_end_to_end_machine_learning_project.ipynb b/02_end_to_end_machine_learning_project.ipynb
index 72a2a8a..990fc3b 100644
--- a/02_end_to_end_machine_learning_project.ipynb
+++ b/02_end_to_end_machine_learning_project.ipynb
@@ -11,6 +11,17 @@
     "*This notebook contains all the sample code and solutions to the exercices in chapter 2.*"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/02_end_to_end_machine_learning_project.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -491,9 +502,25 @@
    "execution_count": 36,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# Download the California image\n",
+    "images_path = os.path.join(PROJECT_ROOT_DIR, \"images\", \"end_to_end_project\")\n",
+    "os.makedirs(images_path, exist_ok=True)\n",
+    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
+    "filename = \"california.png\"\n",
+    "print(\"Downloading\", filename)\n",
+    "url = DOWNLOAD_ROOT + \"images/end_to_end_project/\" + filename\n",
+    "urllib.request.urlretrieve(url, os.path.join(images_path, filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import matplotlib.image as mpimg\n",
-    "california_img=mpimg.imread(PROJECT_ROOT_DIR + '/images/end_to_end_project/california.png')\n",
+    "california_img=mpimg.imread(os.path.join(images_path, filename))\n",
     "ax = housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", figsize=(10,7),\n",
     "                       s=housing['population']/100, label=\"Population\",\n",
     "                       c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"),\n",
@@ -517,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -526,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -535,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -550,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -562,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -573,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -583,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -595,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -611,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -621,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -631,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -640,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -649,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -659,7 +686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -668,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -685,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -695,7 +722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -704,7 +731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -720,7 +747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -736,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -745,7 +772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -755,7 +782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -764,7 +791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -773,7 +800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -783,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -799,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -809,7 +836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -822,7 +849,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -831,7 +858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -851,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -867,7 +894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -878,7 +905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -894,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -924,7 +951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -944,7 +971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -962,7 +989,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -971,7 +998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -990,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -999,7 +1026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1015,7 +1042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1040,7 +1067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1062,7 +1089,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1076,7 +1103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1093,7 +1120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1109,7 +1136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1121,7 +1148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1142,7 +1169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1151,7 +1178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1160,7 +1187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1174,7 +1201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1186,7 +1213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1198,7 +1225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1217,7 +1244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1230,7 +1257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1244,7 +1271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1263,7 +1290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1275,7 +1302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1287,7 +1314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1301,7 +1328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1311,7 +1338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1327,7 +1354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1357,7 +1384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1366,7 +1393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1382,7 +1409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1393,7 +1420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1402,7 +1429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1422,7 +1449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1433,7 +1460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1443,7 +1470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1457,7 +1484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1475,7 +1502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1491,7 +1518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1513,7 +1540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1533,7 +1560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1558,7 +1585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1580,7 +1607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1589,7 +1616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1608,7 +1635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1646,7 +1673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1672,7 +1699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1690,7 +1717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1720,7 +1747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1753,7 +1780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1771,7 +1798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1794,7 +1821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1819,7 +1846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1858,7 +1885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1894,7 +1921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1910,7 +1937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1920,7 +1947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1936,7 +1963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1952,7 +1979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1964,7 +1991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1980,7 +2007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1996,7 +2023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2026,7 +2053,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2039,7 +2066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2055,7 +2082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2089,7 +2116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2105,7 +2132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2143,7 +2170,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   },
   "nav_menu": {
    "height": "279px",
diff --git a/03_classification.ipynb b/03_classification.ipynb
index 2c2200c..048db1e 100644
--- a/03_classification.ipynb
+++ b/03_classification.ipynb
@@ -9,6 +9,17 @@
     "_This notebook contains all the sample code and solutions to the exercises in chapter 3._"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/03_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2522,7 +2533,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   },
   "nav_menu": {},
   "toc": {
diff --git a/04_training_linear_models.ipynb b/04_training_linear_models.ipynb
index 2b5235d..58bf27b 100644
--- a/04_training_linear_models.ipynb
+++ b/04_training_linear_models.ipynb
@@ -14,6 +14,17 @@
     "_This notebook contains all the sample code and solutions to the exercises in chapter 4._"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/04_training_linear_models.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1786,7 +1797,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.3"
   },
   "nav_menu": {},
   "toc": {
diff --git a/05_support_vector_machines.ipynb b/05_support_vector_machines.ipynb
index a99b181..b3a8af5 100644
--- a/05_support_vector_machines.ipynb
+++ b/05_support_vector_machines.ipynb
@@ -9,6 +9,17 @@
     "_This notebook contains all the sample code and solutions to the exercises in chapter 5._"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/05_support_vector_machines.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1823,7 +1834,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   },
   "nav_menu": {},
   "toc": {
diff --git a/06_decision_trees.ipynb b/06_decision_trees.ipynb
index ca5962f..17688bf 100644
--- a/06_decision_trees.ipynb
+++ b/06_decision_trees.ipynb
@@ -14,6 +14,17 @@
     "_This notebook contains all the sample code and solutions to the exercises in chapter 6._"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/06_decision_trees.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -722,7 +733,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   },
   "nav_menu": {
    "height": "309px",
diff --git a/07_ensemble_learning_and_random_forests.ipynb b/07_ensemble_learning_and_random_forests.ipynb
index 19e2584..005d9c4 100644
--- a/07_ensemble_learning_and_random_forests.ipynb
+++ b/07_ensemble_learning_and_random_forests.ipynb
@@ -14,6 +14,17 @@
     "_This notebook contains all the sample code and solutions to the exercises in chapter 7._"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/07_ensemble_learning_and_random_forests.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1383,7 +1394,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   },
   "nav_menu": {
    "height": "252px",
diff --git a/08_dimensionality_reduction.ipynb b/08_dimensionality_reduction.ipynb
index d5f97b7..aef9dc8 100644
--- a/08_dimensionality_reduction.ipynb
+++ b/08_dimensionality_reduction.ipynb
@@ -9,6 +9,17 @@
     "_This notebook contains all the sample code and solutions to the exercises in chapter 8._"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/08_dimensionality_reduction.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2253,7 +2264,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,
diff --git a/09_unsupervised_learning.ipynb b/09_unsupervised_learning.ipynb
index ce7f7b6..6b72618 100644
--- a/09_unsupervised_learning.ipynb
+++ b/09_unsupervised_learning.ipynb
@@ -9,6 +9,17 @@
     "_This notebook contains all the sample code in chapter 9._"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/09_unsupervised_learning.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1439,9 +1450,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from matplotlib.image import imread\n",
-    "image = imread(os.path.join(\"images\",\"unsupervised_learning\",\"ladybug.png\"))\n",
-    "image.shape"
+    "# Download the ladybug image\n",
+    "images_path = os.path.join(PROJECT_ROOT_DIR, \"images\", \"unsupervised_learning\")\n",
+    "os.makedirs(images_path, exist_ok=True)\n",
+    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
+    "filename = \"ladybug.png\"\n",
+    "print(\"Downloading\", filename)\n",
+    "url = DOWNLOAD_ROOT + \"images/unsupervised_learning/\" + filename\n",
+    "urllib.request.urlretrieve(url, os.path.join(images_path, filename))"
    ]
   },
   {
@@ -1449,6 +1465,17 @@
    "execution_count": 73,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "from matplotlib.image import imread\n",
+    "image = imread(os.path.join(images_path, filename))\n",
+    "image.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "X = image.reshape(-1, 3)\n",
     "kmeans = KMeans(n_clusters=8, random_state=42).fit(X)\n",
@@ -1458,7 +1485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1472,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1510,7 +1537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1519,7 +1546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1535,7 +1562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1544,7 +1571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1560,7 +1587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1569,7 +1596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1579,7 +1606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1595,7 +1622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1604,7 +1631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1617,7 +1644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1626,7 +1653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1642,7 +1669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1651,7 +1678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1662,7 +1689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1671,7 +1698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 91,
    "metadata": {
     "scrolled": false
    },
@@ -1710,7 +1737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1719,7 +1746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1737,7 +1764,7 @@
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    "source": [
@@ -1746,7 +1773,7 @@
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    "metadata": {},
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    "source": [
@@ -1765,7 +1792,7 @@
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-   "execution_count": 95,
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    "metadata": {},
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    "source": [
@@ -1781,7 +1808,7 @@
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    "metadata": {},
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    "source": [
@@ -1802,7 +1829,7 @@
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    "source": [
@@ -1827,7 +1854,7 @@
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    "source": [
@@ -1838,7 +1865,7 @@
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    "source": [
@@ -1848,7 +1875,7 @@
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    "metadata": {},
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    "source": [
@@ -1864,7 +1891,7 @@
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-   "execution_count": 101,
+   "execution_count": 102,
    "metadata": {},
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    "source": [
@@ -1881,7 +1908,7 @@
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-   "execution_count": 102,
+   "execution_count": 103,
    "metadata": {},
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    "source": [
@@ -1892,7 +1919,7 @@
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-   "execution_count": 103,
+   "execution_count": 104,
    "metadata": {},
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    "source": [
@@ -1902,7 +1929,7 @@
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    "source": [
@@ -1925,7 +1952,7 @@
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    "metadata": {},
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    "source": [
@@ -1950,7 +1977,7 @@
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-   "execution_count": 106,
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    "metadata": {},
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    "source": [
@@ -1959,7 +1986,7 @@
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    "metadata": {},
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    "source": [
@@ -1968,7 +1995,7 @@
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    "metadata": {},
    "outputs": [],
    "source": [
@@ -1977,7 +2004,7 @@
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    "metadata": {},
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    "source": [
@@ -1987,7 +2014,7 @@
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-   "execution_count": 110,
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    "metadata": {},
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    "source": [
@@ -1996,7 +2023,7 @@
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   {
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-   "execution_count": 111,
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    "metadata": {},
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    "source": [
@@ -2005,7 +2032,7 @@
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-   "execution_count": 112,
+   "execution_count": 113,
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    "source": [
@@ -2014,7 +2041,7 @@
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-   "execution_count": 113,
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    "source": [
@@ -2023,7 +2050,7 @@
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-   "execution_count": 114,
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    "source": [
@@ -2032,7 +2059,7 @@
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    "source": [
@@ -2042,7 +2069,7 @@
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    "source": [
@@ -2075,7 +2102,7 @@
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    "source": [
@@ -2093,7 +2120,7 @@
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    "source": [
@@ -2102,7 +2129,7 @@
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    "source": [
@@ -2111,7 +2138,7 @@
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    "source": [
@@ -2121,7 +2148,7 @@
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    "source": [
@@ -2131,7 +2158,7 @@
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    "metadata": {},
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    "source": [
@@ -2140,7 +2167,7 @@
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    "metadata": {},
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    "source": [
@@ -2153,7 +2180,7 @@
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    "metadata": {},
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    "source": [
@@ -2179,7 +2206,7 @@
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    "metadata": {},
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    "source": [
@@ -2188,7 +2215,7 @@
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    "metadata": {},
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    "source": [
@@ -2198,7 +2225,7 @@
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    "metadata": {},
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    "source": [
@@ -2208,7 +2235,7 @@
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    "metadata": {},
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    "source": [
@@ -2217,7 +2244,7 @@
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    "metadata": {},
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    "source": [
@@ -2239,7 +2266,7 @@
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    "metadata": {},
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    "source": [
@@ -2263,7 +2290,7 @@
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    "metadata": {},
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    "source": [
@@ -2272,7 +2299,7 @@
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    "metadata": {},
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    "source": [
@@ -2282,7 +2309,7 @@
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    "metadata": {},
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    "source": [
@@ -2293,7 +2320,7 @@
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    "source": [
@@ -2302,7 +2329,7 @@
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    "metadata": {
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@@ -2320,7 +2347,7 @@
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    "metadata": {},
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    "source": [
@@ -2341,7 +2368,7 @@
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@@ -2350,7 +2377,7 @@
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    "source": [
@@ -2367,7 +2394,7 @@
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    "metadata": {},
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    "source": [
@@ -2376,7 +2403,7 @@
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    "metadata": {},
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@@ -2385,7 +2412,7 @@
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    "metadata": {},
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    "source": [
@@ -2401,7 +2428,7 @@
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    "metadata": {},
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    "source": [
@@ -2417,7 +2444,7 @@
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    "metadata": {},
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    "source": [
@@ -2433,7 +2460,7 @@
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    "metadata": {},
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    "source": [
@@ -2442,7 +2469,7 @@
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    "metadata": {},
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    "source": [
@@ -2458,7 +2485,7 @@
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    "metadata": {},
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    "source": [
@@ -2468,7 +2495,7 @@
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    "metadata": {},
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    "source": [
@@ -2491,7 +2518,7 @@
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    "metadata": {},
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    "source": [
@@ -2507,7 +2534,7 @@
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    "metadata": {},
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    "source": [
@@ -2530,7 +2557,7 @@
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    "metadata": {},
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    "source": [
@@ -2569,7 +2596,7 @@
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    "metadata": {},
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    "source": [
@@ -2594,7 +2621,7 @@
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    "metadata": {},
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    "source": [
@@ -2610,7 +2637,7 @@
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    "metadata": {},
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    "source": [
@@ -2628,7 +2655,7 @@
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    "metadata": {},
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    "source": [
@@ -2640,7 +2667,7 @@
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    "metadata": {},
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    "source": [
@@ -2665,7 +2692,7 @@
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    "source": [
@@ -2676,7 +2703,7 @@
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    "source": [
@@ -2716,7 +2743,7 @@
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    "source": [
@@ -2725,7 +2752,7 @@
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    "source": [
@@ -2741,7 +2768,7 @@
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    "source": [
@@ -2758,7 +2785,7 @@
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    "source": [
@@ -2767,7 +2794,7 @@
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    "source": [
@@ -2790,7 +2817,7 @@
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    "metadata": {},
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    "source": [
@@ -2800,7 +2827,7 @@
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    "source": [
@@ -2810,7 +2837,7 @@
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    "source": [
@@ -2841,7 +2868,7 @@
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    "source": [
@@ -2860,7 +2887,7 @@
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    "source": [
@@ -2869,7 +2896,7 @@
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    "source": [
@@ -2892,7 +2919,7 @@
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    "source": [
@@ -2901,7 +2928,7 @@
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    "source": [
@@ -2918,7 +2945,7 @@
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@@ -2927,7 +2954,7 @@
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    "source": [
@@ -2938,7 +2965,7 @@
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    "source": [
@@ -2953,7 +2980,7 @@
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    "source": [
@@ -2962,7 +2989,7 @@
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    "source": [
@@ -2971,7 +2998,7 @@
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@@ -2998,7 +3025,7 @@
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@@ -3007,7 +3034,7 @@
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    "metadata": {
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@@ -3019,7 +3046,7 @@
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    "source": [
@@ -3053,7 +3080,7 @@
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@@ -3062,7 +3089,7 @@
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@@ -3075,7 +3102,7 @@
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@@ -3163,7 +3190,7 @@
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