diff --git a/13_loading_and_preprocessing_data.ipynb b/13_loading_and_preprocessing_data.ipynb
index ed70265..12257f1 100644
--- a/13_loading_and_preprocessing_data.ipynb
+++ b/13_loading_and_preprocessing_data.ipynb
@@ -9,6 +9,17 @@
     "_This notebook contains all the sample code and solutions to the exercises in chapter 13._"
    ]
   },
+  {
+   "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/13_loading_and_preprocessing_data.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": {},
@@ -20,7 +31,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview."
+    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0."
    ]
   },
   {
@@ -37,7 +48,15 @@
     "import sklearn\n",
     "assert sklearn.__version__ >= \"0.20\"\n",
     "\n",
-    "# TensorFlow ≥2.0-preview is required\n",
+    "try:\n",
+    "    # %tensorflow_version only exists in Colab.\n",
+    "    %tensorflow_version 2.x\n",
+    "    !pip install -q -U tfx==0.15.0rc0\n",
+    "    print(\"You can safely ignore the package incompatibility errors.\")\n",
+    "except Exception:\n",
+    "    pass\n",
+    "\n",
+    "# TensorFlow ≥2.0 is required\n",
     "import tensorflow as tf\n",
     "from tensorflow import keras\n",
     "assert tf.__version__ >= \"2.0\"\n",
@@ -1379,8 +1398,7 @@
     "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
     "\n",
     "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
-    "    if not os.path.isdir(housing_path):\n",
-    "        os.makedirs(housing_path)\n",
+    "    os.makedirs(housing_path, exist_ok=True)\n",
     "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
     "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
     "    housing_tgz = tarfile.open(tgz_path)\n",
@@ -1747,18 +1765,6 @@
     "model.fit(mnist_train, steps_per_epoch=60000 // 32, epochs=5)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    datasets = tfds.load(\"imagenet2012\", split=[\"train\", \"test\"])\n",
-    "except AssertionError as ex:\n",
-    "    print(ex)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1768,7 +1774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1787,7 +1793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1797,7 +1803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1828,7 +1834,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   },
   "nav_menu": {
    "height": "264px",