diff --git a/files/dl/apprentissage/cours/kmeans/kmeans.ipynb b/files/dl/apprentissage/cours/kmeans/kmeans.ipynb
index bb454035..0a944238 100644
--- a/files/dl/apprentissage/cours/kmeans/kmeans.ipynb
+++ b/files/dl/apprentissage/cours/kmeans/kmeans.ipynb
@@ -1 +1 @@
-{"cells": [{"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": ["# Algorithme des k-moyennes\n\n", "\n", "\n", "\n", "[Visualisation de l'algorithme des k-moyennes](https://www.naftaliharris.com/blog/visualizing-k-means-clustering)\n", "\n", "## G\u00e9n\u00e9ration des donn\u00e9es\n", "\n", "On g\u00e9n\u00e8re des points al\u00e9atoirement, ainsi que k = 4 centres initialement al\u00e9atoires :"]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [{"data": {"image/png": 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", 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