diff --git a/06_decision_trees.ipynb b/06_decision_trees.ipynb
index b609a5c..b956c61 100644
--- a/06_decision_trees.ipynb
+++ b/06_decision_trees.ipynb
@@ -25,7 +25,7 @@
    "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)."
+    "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."
    ]
   },
   {
@@ -38,6 +38,10 @@
     "import sys\n",
     "assert sys.version_info >= (3, 5)\n",
     "\n",
+    "# Scikit-Learn ≥0.20 is required\n",
+    "import sklearn\n",
+    "assert sklearn.__version__ >= \"0.20\"\n",
+    "\n",
     "# Common imports\n",
     "import numpy as np\n",
     "import os\n",
@@ -56,32 +60,15 @@
     "# Where to save the figures\n",
     "PROJECT_ROOT_DIR = \".\"\n",
     "CHAPTER_ID = \"decision_trees\"\n",
+    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
+    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
     "\n",
-    "def image_path(fig_id):\n",
-    "    return os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id)\n",
-    "\n",
-    "def save_fig(fig_id, tight_layout=True):\n",
+    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
+    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
     "    print(\"Saving figure\", fig_id)\n",
     "    if tight_layout:\n",
     "        plt.tight_layout()\n",
-    "    plt.savefig(image_path(fig_id) + \".png\", format='png', dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook assumes you have installed Scikit-Learn ≥0.20."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sklearn\n",
-    "assert sklearn.__version__ >= \"0.20\""
+    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
    ]
   },
   {
@@ -93,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,25 +97,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from graphviz import Source\n",
     "from sklearn.tree import export_graphviz\n",
     "\n",
     "export_graphviz(\n",
     "        tree_clf,\n",
-    "        out_file=image_path(\"iris_tree.dot\"),\n",
+    "        out_file=os.path.join(IMAGES_PATH, \"iris_tree.dot\"),\n",
     "        feature_names=iris.feature_names[2:],\n",
     "        class_names=iris.target_names,\n",
     "        rounded=True,\n",
     "        filled=True\n",
-    "    )"
+    "    )\n",
+    "\n",
+    "Source.from_file(os.path.join(IMAGES_PATH, \"iris_tree.dot\"))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -216,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -230,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -247,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -292,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -342,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -354,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -401,19 +391,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
     "export_graphviz(\n",
     "        tree_reg1,\n",
-    "        out_file=image_path(\"regression_tree.dot\"),\n",
+    "        out_file=os.path.join(IMAGES_PATH, \"regression_tree.dot\"),\n",
     "        feature_names=[\"x1\"],\n",
     "        rounded=True,\n",
     "        filled=True\n",
     "    )"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Source.from_file(os.path.join(IMAGES_PATH, \"regression_tree.dot\"))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 18,
diff --git a/07_ensemble_learning_and_random_forests.ipynb b/07_ensemble_learning_and_random_forests.ipynb
index 9a6f179..a47f0a5 100644
--- a/07_ensemble_learning_and_random_forests.ipynb
+++ b/07_ensemble_learning_and_random_forests.ipynb
@@ -25,7 +25,7 @@
    "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)."
+    "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."
    ]
   },
   {
@@ -38,6 +38,10 @@
     "import sys\n",
     "assert sys.version_info >= (3, 5)\n",
     "\n",
+    "# Scikit-Learn ≥0.20 is required\n",
+    "import sklearn\n",
+    "assert sklearn.__version__ >= \"0.20\"\n",
+    "\n",
     "# Common imports\n",
     "import numpy as np\n",
     "import os\n",
@@ -56,32 +60,15 @@
     "# Where to save the figures\n",
     "PROJECT_ROOT_DIR = \".\"\n",
     "CHAPTER_ID = \"ensembles\"\n",
+    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
+    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
     "\n",
-    "def image_path(fig_id):\n",
-    "    return os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id)\n",
-    "\n",
-    "def save_fig(fig_id, tight_layout=True):\n",
+    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
+    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
     "    print(\"Saving figure\", fig_id)\n",
     "    if tight_layout:\n",
     "        plt.tight_layout()\n",
-    "    plt.savefig(image_path(fig_id) + \".png\", format='png', dpi=300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook assumes you have installed Scikit-Learn ≥0.20."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sklearn\n",
-    "assert sklearn.__version__ >= \"0.20\""
+    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
    ]
   },
   {
diff --git a/08_dimensionality_reduction.ipynb b/08_dimensionality_reduction.ipynb
index 1576e2a..dfc66ae 100644
--- a/08_dimensionality_reduction.ipynb
+++ b/08_dimensionality_reduction.ipynb
@@ -20,7 +20,7 @@
    "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)."
+    "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."
    ]
   },
   {
@@ -33,6 +33,10 @@
     "import sys\n",
     "assert sys.version_info >= (3, 5)\n",
     "\n",
+    "# Scikit-Learn ≥0.20 is required\n",
+    "import sklearn\n",
+    "assert sklearn.__version__ >= \"0.20\"\n",
+    "\n",
     "# Common imports\n",
     "import numpy as np\n",
     "import os\n",
@@ -51,36 +55,21 @@
     "# Where to save the figures\n",
     "PROJECT_ROOT_DIR = \".\"\n",
     "CHAPTER_ID = \"dim_reduction\"\n",
+    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
+    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
     "\n",
-    "def save_fig(fig_id, tight_layout=True):\n",
-    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
+    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
+    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
     "    print(\"Saving figure\", fig_id)\n",
     "    if tight_layout:\n",
     "        plt.tight_layout()\n",
-    "    plt.savefig(path, format='png', dpi=300)\n",
+    "    plt.savefig(path, format=fig_extension, dpi=resolution)\n",
     "\n",
     "# Ignore useless warnings (see SciPy issue #5998)\n",
     "import warnings\n",
     "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook assumes you have installed Scikit-Learn ≥0.20."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sklearn\n",
-    "assert sklearn.__version__ >= \"0.20\""
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/09_unsupervised_learning.ipynb b/09_unsupervised_learning.ipynb
index d7cd98b..f9a3a1a 100644
--- a/09_unsupervised_learning.ipynb
+++ b/09_unsupervised_learning.ipynb
@@ -20,7 +20,7 @@
    "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)."
+    "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."
    ]
   },
   {
@@ -33,6 +33,10 @@
     "import sys\n",
     "assert sys.version_info >= (3, 5)\n",
     "\n",
+    "# Scikit-Learn ≥0.20 is required\n",
+    "import sklearn\n",
+    "assert sklearn.__version__ >= \"0.20\"\n",
+    "\n",
     "# Common imports\n",
     "import numpy as np\n",
     "import os\n",
@@ -51,13 +55,15 @@
     "# Where to save the figures\n",
     "PROJECT_ROOT_DIR = \".\"\n",
     "CHAPTER_ID = \"unsupervised_learning\"\n",
+    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
+    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
     "\n",
-    "def save_fig(fig_id, tight_layout=True):\n",
-    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
+    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
+    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
     "    print(\"Saving figure\", fig_id)\n",
     "    if tight_layout:\n",
     "        plt.tight_layout()\n",
-    "    plt.savefig(path, format='png', dpi=300)\n",
+    "    plt.savefig(path, format=fig_extension, dpi=resolution)\n",
     "\n",
     "# Ignore useless warnings (see SciPy issue #5998)\n",
     "import warnings\n",
diff --git a/10_neural_nets_with_keras.ipynb b/10_neural_nets_with_keras.ipynb
index 7d15449..0f507c4 100644
--- a/10_neural_nets_with_keras.ipynb
+++ b/10_neural_nets_with_keras.ipynb
@@ -20,7 +20,7 @@
    "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)."
+    "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."
    ]
   },
   {
@@ -33,6 +33,14 @@
     "import sys\n",
     "assert sys.version_info >= (3, 5)\n",
     "\n",
+    "# Scikit-Learn ≥0.20 is required\n",
+    "import sklearn\n",
+    "assert sklearn.__version__ >= \"0.20\"\n",
+    "\n",
+    "# TensorFlow ≥2.0-preview is required\n",
+    "import tensorflow as tf\n",
+    "assert hasattr(tf.compat, \"v1\")\n",
+    "\n",
     "# Common imports\n",
     "import numpy as np\n",
     "import os\n",
@@ -51,13 +59,15 @@
     "# Where to save the figures\n",
     "PROJECT_ROOT_DIR = \".\"\n",
     "CHAPTER_ID = \"ann\"\n",
+    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
+    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
     "\n",
-    "def save_fig(fig_id, tight_layout=True):\n",
-    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
+    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
+    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
     "    print(\"Saving figure\", fig_id)\n",
     "    if tight_layout:\n",
     "        plt.tight_layout()\n",
-    "    plt.savefig(path, format='png', dpi=300)\n",
+    "    plt.savefig(path, format=fig_extension, dpi=resolution)\n",
     "\n",
     "# Ignore useless warnings (see SciPy issue #5998)\n",
     "import warnings\n",