Quarto output

notebooks/01_classification.ipynb

@@ -4,22 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "---\n",
-    "title: Classification\n",
-    "subtitle: Finding forests with satelite imagery\n",
-    "jupyter: \n",
-    "  kernelspec:\n",
-    "    name: \"01_classification\"\n",
-    "    language: \"python\"\n",
-    "    display_name: \"01_classification\"\n",
-    "format: \n",
-    "    html:\n",
-    "        code-fold: show\n",
-    "eval: true\n",
-    "---\n",
-    "\n",
-    "## Data Acquisition\n",
-    "In this chapter, we will employ machine learning techniques to classify a scene using satellite imagery. Specifically, we will utilize ``scikit-learn`` to implement two distinct classifiers and subsequently compare their results. To begin, we need to import the following modules."
+    "# Classification\n",
+    "Finding forests with satelite imagery\n"
    ]
   },
   {

notebooks/02_floodmapping.ipynb

@@ -4,17 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "---\n",
-    "title: Reverend Bayes updates our Belief in Flood Detection\n",
-    "subtitle: How an 275 year old idea helps map the extent of floods\n",
-    "jupyter: \n",
-    "  kernelspec:\n",
-    "    name: \"02_floodmapping\"\n",
-    "    language: \"python\"\n",
-    "    display_name: \"02_floodmapping\"\n",
-    "---\n",
-    "\n",
-    "![Image from [wikipedia](https://upload.wikimedia.org/wikipedia/commons/d/d4/Thomas_Bayes.gif)](https://upload.wikimedia.org/wikipedia/commons/d/d4/Thomas_Bayes.gif)"
+    "# Reverend Bayes updates our Belief in Flood Detection\n",
+    "How an 275 year old idea helps map the extent of floods\n"
    ]
   },
   {

notebooks/references.ipynb

@@ -4,10 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# References\n",
+    "# References {.unnumbered}\n",
     "\n",
-    "```{bibliography}\n",
-    "```\n"
+    "::: {#refs}\n",
+    ":::"
    ]
   }
  ],