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nitrain · May 29, 2024 · 10071fe · 10071fe
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diff --git a/book/02-03.ipynb b/book/02-03.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 8. Cloud storage"
+    "# 7. Cloud storage"
    ]
   },
   {

diff --git a/book/02-04.ipynb b/book/02-04.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 9. Applying fixed transforms"
+    "# 8. Applying fixed transforms"
    ]
   },
   {

diff --git a/book/03-01.ipynb b/book/03-01.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Using loaders\n",
+    "# 9. Using loaders\n",
     "\n",
     "In the last section, you learned about how to map entire datasets of medical images and related meta-data from data stored in memory, locally in folders, or in the cloud. You also saw how to attached a wide variety nitrain transforms to pre-process your images every time they're returned from your dataset. The nitrain dataset paradigm will be useful to you even beyond traditional AI tasks.\n",
     "\n",

diff --git a/book/03-02.ipynb b/book/03-02.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Multiple inputs or outputs"
+    "# 10. Further with samplers"
    ]
   },
   {

diff --git a/book/03-03.ipynb b/book/03-03.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Applying random transforms"
+    "# 11. Applying random transforms"
    ]
   },
   {

diff --git a/book/03-04.ipynb b/book/03-04.ipynb