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@nicofarr
Last active June 22, 2017 14:36
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{
"cells": [
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import sklearn\n",
"import matplotlib.pyplot as plt \n",
"%matplotlib inline "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Un gros titre \n",
"--\n",
"\n",
"$K=\\alpha \\times \\beta $\n",
"Là tu peux écrire ce que tu veux \n",
"\n",
"On peut mettre des [liens](http://www.brain.bzh)\n",
"\n",
"Voilà comment mettre des trucs en **gras**"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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VW4ZxXt8OTscSOW0qbglIn27eT0paOoUHq7hxVHemXtyPls307S6BQd/JElBKK+t4aFk2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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f80a7386fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(range(10))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
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}
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