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Make notebooks 1 to 9 runnable in Colab without changes

main
Aurélien Geron 2019-11-05 22:26:52 +08:00
parent 7e35fdc3c4
commit a55720e9e4
9 changed files with 400 additions and 254 deletions
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92
01_the_machine_learning_landscape.ipynb
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@ -9,6 +9,17 @@
"_This is the code used to generate some of the figures in chapter 1._" "_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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -111,6 +122,21 @@
"execution_count": 6, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [], "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": [ "source": [
"# Code example\n", "# Code example\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
@ -201,7 +227,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -228,7 +254,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -253,7 +279,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -265,7 +291,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -288,7 +314,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -301,7 +327,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -312,7 +338,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -321,7 +347,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -334,7 +360,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -360,7 +386,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -369,7 +395,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -378,7 +404,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 19,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -403,7 +429,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 20,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -418,7 +444,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 21,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -435,7 +461,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 22,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -447,7 +473,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 23,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -467,7 +493,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 24,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -476,7 +502,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 24, "execution_count": 25,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -485,7 +511,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 26,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -506,7 +532,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 27,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -543,7 +569,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 28,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -552,7 +578,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 28, "execution_count": 29,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -561,7 +587,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 29, "execution_count": 30,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -578,7 +604,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 30, "execution_count": 31,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -610,7 +636,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 32,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -635,7 +661,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 33,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -644,7 +670,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 33, "execution_count": 34,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -653,7 +679,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 34, "execution_count": 35,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -685,7 +711,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 35, "execution_count": 36,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -697,7 +723,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 36, "execution_count": 37,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -708,7 +734,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 37, "execution_count": 38,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -719,7 +745,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 38, "execution_count": 39,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -758,7 +784,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.8" "version": "3.7.3"
}, },
"nav_menu": {}, "nav_menu": {},
"toc": { "toc": {

231
02_end_to_end_machine_learning_project.ipynb
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@ -11,6 +11,17 @@
"*This notebook contains all the sample code and solutions to the exercices in chapter 2.*" "*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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -491,9 +502,25 @@
"execution_count": 36, "execution_count": 36,
"metadata": {}, "metadata": {},
"outputs": [], "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": [ "source": [
"import matplotlib.image as mpimg\n", "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", "ax = housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", figsize=(10,7),\n",
" s=housing['population']/100, label=\"Population\",\n", " s=housing['population']/100, label=\"Population\",\n",
" c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"),\n", " c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"),\n",
@ -517,7 +544,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 37, "execution_count": 38,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -526,7 +553,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 38, "execution_count": 39,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -535,7 +562,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 39, "execution_count": 40,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -550,7 +577,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 40, "execution_count": 41,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -562,7 +589,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 41, "execution_count": 42,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -573,7 +600,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 42, "execution_count": 43,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -583,7 +610,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 43, "execution_count": 44,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -595,7 +622,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 44, "execution_count": 45,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -611,7 +638,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 45, "execution_count": 46,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -621,7 +648,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 46, "execution_count": 47,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -631,7 +658,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 47, "execution_count": 48,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -640,7 +667,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 48, "execution_count": 49,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -649,7 +676,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 49, "execution_count": 50,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -659,7 +686,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 50, "execution_count": 51,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -668,7 +695,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 51, "execution_count": 52,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -685,7 +712,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 52, "execution_count": 53,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -695,7 +722,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 53, "execution_count": 54,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -704,7 +731,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 54, "execution_count": 55,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -720,7 +747,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 55, "execution_count": 56,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -736,7 +763,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 56, "execution_count": 57,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -745,7 +772,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 57, "execution_count": 58,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -755,7 +782,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 58, "execution_count": 59,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -764,7 +791,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 59, "execution_count": 60,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -773,7 +800,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 60, "execution_count": 61,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -783,7 +810,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 61, "execution_count": 62,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -799,7 +826,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 62, "execution_count": 63,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -809,7 +836,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 63, "execution_count": 64,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -822,7 +849,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 64, "execution_count": 65,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -831,7 +858,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 65, "execution_count": 66,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -851,7 +878,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 66, "execution_count": 67,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -867,7 +894,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 67, "execution_count": 68,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -878,7 +905,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 68, "execution_count": 69,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -894,7 +921,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 69, "execution_count": 70,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -924,7 +951,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 70, "execution_count": 71,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -944,7 +971,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 71, "execution_count": 72,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -962,7 +989,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 72, "execution_count": 73,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -971,7 +998,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 73, "execution_count": 74,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -990,7 +1017,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 74, "execution_count": 75,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -999,7 +1026,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 75, "execution_count": 76,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1015,7 +1042,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 76, "execution_count": 77,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1040,7 +1067,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 77, "execution_count": 78,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1062,7 +1089,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 78, "execution_count": 79,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1076,7 +1103,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 79, "execution_count": 80,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1093,7 +1120,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 80, "execution_count": 81,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1109,7 +1136,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 81, "execution_count": 82,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1121,7 +1148,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 82, "execution_count": 83,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1142,7 +1169,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 83, "execution_count": 84,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1151,7 +1178,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 84, "execution_count": 85,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1160,7 +1187,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 85, "execution_count": 86,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1174,7 +1201,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 86, "execution_count": 87,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1186,7 +1213,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 87, "execution_count": 88,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1198,7 +1225,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 88, "execution_count": 89,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1217,7 +1244,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 89, "execution_count": 90,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1230,7 +1257,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 90, "execution_count": 91,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1244,7 +1271,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 91, "execution_count": 92,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1263,7 +1290,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 92, "execution_count": 93,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1275,7 +1302,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 93, "execution_count": 94,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1287,7 +1314,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 94, "execution_count": 95,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1301,7 +1328,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 95, "execution_count": 96,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1311,7 +1338,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 96, "execution_count": 97,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1327,7 +1354,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 97, "execution_count": 98,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1357,7 +1384,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 98, "execution_count": 99,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1366,7 +1393,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 99, "execution_count": 100,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1382,7 +1409,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 100, "execution_count": 101,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1393,7 +1420,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 101, "execution_count": 102,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1402,7 +1429,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 102, "execution_count": 103,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1422,7 +1449,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 103, "execution_count": 104,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1433,7 +1460,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 104, "execution_count": 105,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1443,7 +1470,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 105, "execution_count": 106,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1457,7 +1484,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 106, "execution_count": 107,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1475,7 +1502,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 107, "execution_count": 108,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1491,7 +1518,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 108, "execution_count": 109,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1513,7 +1540,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 109, "execution_count": 110,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1533,7 +1560,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 110, "execution_count": 111,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1558,7 +1585,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 111, "execution_count": 112,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1580,7 +1607,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 112, "execution_count": 113,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1589,7 +1616,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 113, "execution_count": 114,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1608,7 +1635,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 114, "execution_count": 115,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1646,7 +1673,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 115, "execution_count": 116,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1672,7 +1699,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 116, "execution_count": 117,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1690,7 +1717,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 117, "execution_count": 118,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1720,7 +1747,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 118, "execution_count": 119,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1753,7 +1780,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 119, "execution_count": 120,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1771,7 +1798,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 120, "execution_count": 121,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1794,7 +1821,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 121, "execution_count": 122,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1819,7 +1846,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 122, "execution_count": 123,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1858,7 +1885,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 123, "execution_count": 124,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1894,7 +1921,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 124, "execution_count": 125,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1910,7 +1937,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 125, "execution_count": 126,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1920,7 +1947,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 126, "execution_count": 127,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1936,7 +1963,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 127, "execution_count": 128,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1952,7 +1979,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 128, "execution_count": 129,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1964,7 +1991,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 129, "execution_count": 130,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1980,7 +2007,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 130, "execution_count": 131,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1996,7 +2023,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 131, "execution_count": 132,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -2026,7 +2053,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 132, "execution_count": 133,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -2039,7 +2066,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 133, "execution_count": 134,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -2055,7 +2082,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 134, "execution_count": 135,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -2089,7 +2116,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 135, "execution_count": 136,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -2105,7 +2132,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 136, "execution_count": 137,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -2143,7 +2170,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.4" "version": "3.7.3"
}, },
"nav_menu": { "nav_menu": {
"height": "279px", "height": "279px",

13
03_classification.ipynb
View File

@ -9,6 +9,17 @@
"_This notebook contains all the sample code and solutions to the exercises in chapter 3._" "_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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -2522,7 +2533,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.4" "version": "3.7.3"
}, },
"nav_menu": {}, "nav_menu": {},
"toc": { "toc": {

13
04_training_linear_models.ipynb
View File

@ -14,6 +14,17 @@
"_This notebook contains all the sample code and solutions to the exercises in chapter 4._" "_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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -1786,7 +1797,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.8" "version": "3.7.3"
}, },
"nav_menu": {}, "nav_menu": {},
"toc": { "toc": {

13
05_support_vector_machines.ipynb
View File

@ -9,6 +9,17 @@
"_This notebook contains all the sample code and solutions to the exercises in chapter 5._" "_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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -1823,7 +1834,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.4" "version": "3.7.3"
}, },
"nav_menu": {}, "nav_menu": {},
"toc": { "toc": {

13
06_decision_trees.ipynb
View File

@ -14,6 +14,17 @@
"_This notebook contains all the sample code and solutions to the exercises in chapter 6._" "_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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -722,7 +733,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.4" "version": "3.7.3"
}, },
"nav_menu": { "nav_menu": {
"height": "309px", "height": "309px",

13
07_ensemble_learning_and_random_forests.ipynb
View File

@ -14,6 +14,17 @@
"_This notebook contains all the sample code and solutions to the exercises in chapter 7._" "_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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -1383,7 +1394,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.4" "version": "3.7.3"
}, },
"nav_menu": { "nav_menu": {
"height": "252px", "height": "252px",

13
08_dimensionality_reduction.ipynb
View File

@ -9,6 +9,17 @@
"_This notebook contains all the sample code and solutions to the exercises in chapter 8._" "_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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -2253,7 +2264,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.8" "version": "3.7.3"
} }
}, },
"nbformat": 4, "nbformat": 4,

253
09_unsupervised_learning.ipynb
View File

@ -9,6 +9,17 @@
"_This notebook contains all the sample code in chapter 9._" "_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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -1439,9 +1450,14 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from matplotlib.image import imread\n", "# Download the ladybug image\n",
"image = imread(os.path.join(\"images\",\"unsupervised_learning\",\"ladybug.png\"))\n", "images_path = os.path.join(PROJECT_ROOT_DIR, \"images\", \"unsupervised_learning\")\n",
"image.shape" "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, "execution_count": 73,
"metadata": {}, "metadata": {},
"outputs": [], "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": [ "source": [
"X = image.reshape(-1, 3)\n", "X = image.reshape(-1, 3)\n",
"kmeans = KMeans(n_clusters=8, random_state=42).fit(X)\n", "kmeans = KMeans(n_clusters=8, random_state=42).fit(X)\n",
@ -1458,7 +1485,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 74, "execution_count": 75,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1472,7 +1499,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 75, "execution_count": 76,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1510,7 +1537,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 76, "execution_count": 77,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
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@ -2927,7 +2954,7 @@
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{ {
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"source": [ "source": [
@ -2938,7 +2965,7 @@
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{ {
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"source": [ "source": [
@ -2953,7 +2980,7 @@
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{ {
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"source": [ "source": [
@ -2962,7 +2989,7 @@
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{ {
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"source": [ "source": [
@ -2971,7 +2998,7 @@
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{ {
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"source": [ "source": [
@ -2998,7 +3025,7 @@
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{ {
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"source": [ "source": [
@ -3007,7 +3034,7 @@
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{ {
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"metadata": { "metadata": {
"scrolled": true "scrolled": true
}, },
@ -3019,7 +3046,7 @@
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{ {
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"source": [ "source": [
@ -3053,7 +3080,7 @@
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{ {
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"source": [ "source": [
@ -3062,7 +3089,7 @@
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{ {
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"source": [ "source": [
@ -3075,7 +3102,7 @@
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{ {
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"source": [ "source": [
@ -3163,7 +3190,7 @@
"name": "python", "name": "python",
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} }
}, },
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