redo recent updates

Author: MelissaGraham

DP0.2/19a_Introduction_to_AI/19a_Introduction_to_AI.ipynb

@@ -7,8 +7,8 @@
     "<img align=\"left\" src = https://project.lsst.org/sites/default/files/Rubin-O-Logo_0.png width=250 style=\"padding: 10px\"> \n",
     "<br><b> Introduction to AI-based Image Classification with Pytorch</b> <br>\n",
     "Contact author: Brian Nord <br>\n",
-    "Last verified to run: 2025-06-20 <br>\n",
-    "LSST Science Pipelines version: Release r29.1.0 <br>\n",
+    "Last verified to run: 2025-10-30 <br>\n",
+    "LSST Science Pipelines version: Release r29.2.0 <br>\n",
     "Container size: medium <br>\n",
     "Targeted learning level: beginner <br>"
    ]
@@ -1421,7 +1421,9 @@
    "source": [
     "Plot the distributions of pixel values to understand the data further. All pixel data has been normalized, and pixels have values between 0 and 1 only.\n",
     "\n",
-    "The distribution of pixel values matches the images shown above: mostly black (values near 0) pixels, with some white (values near 1), and a few grey (values in between 0 and 1)."
+    "The distribution of pixel values matches the images shown above: mostly black (values near 0) pixels, with some white (values near 1), and a few grey (values in between 0 and 1).\n",
+    "\n",
+    "> **Warning:** the following cell produces a DeprecationWarning that is ok to ignore."
    ]
   },
   {
@@ -2293,7 +2295,9 @@
    "source": [
     "The Receiver Operator Characteristic (ROC) Curve is a plot of the True Positive Rate (TPR; y axis) versus the False Positive Rate (FPR; x axis).\n",
     "\n",
-    "For a given classification threshold on the probability (canonically, 0.5), the balance of true positives and false positives will change."
+    "For a given classification threshold on the probability (canonically, 0.5), the balance of true positives and false positives will change.\n",
+    "\n",
+    "> **Warning:** the cell below produces a pink FutureWarning about deprecated kwargs that is ok to ignore."
    ]
   },
   {
@@ -2327,14 +2331,14 @@
     "id": "vvAqrZwjVYBt"
    },
    "source": [
-    "### 4.5. Investigating predictions in detail"
+    "### 4.6. Investigating predictions in detail"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 4.5.1. Glossary: classification metrics"
+    "#### 4.6.1. Glossary: classification metrics"
    ]
   },
   {
@@ -2353,7 +2357,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 4.5.2. Explore the classification of the training data for an example class value"
+    "#### 4.6.2. Explore the classification of the training data for an example class value"
    ]
   },
   {
@@ -2567,7 +2571,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 4.5.3. Investigate morphological features of images with feature maps"
+    "#### 4.6.3. Investigate morphological features of images with feature maps"
    ]
   },
   {