add starters for OOD-distance and OOD-algo

code/7c-OOD-detection-algo-design.ipynb

@@ -0,0 +1,66 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "---\n",
+        "title: \"OOD Detection: Training-Time Regularization\"\n",
+        "teaching: 0\n",
+        "exercises: 0\n",
+        "---"
+      ],
+      "metadata": {
+        "id": "JVTw2HExPXHz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Questions\n",
+        "- What are the key considerations when designing algorithms for OOD detection?\n",
+        "- How can OOD detection be incorporated into the loss functions of models?\n",
+        "- What are the challenges and best practices for training models with OOD detection capabilities?"
+      ],
+      "metadata": {
+        "id": "4Kmnv3SfbJzY"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Objectives\n",
+        "- Understand the critical design considerations for creating effective OOD detection algorithms.\n",
+        "- Learn how to integrate OOD detection into the loss functions of machine learning models.\n",
+        "- Identify the challenges in training models with OOD detection and explore best practices to overcome these challenges."
+      ],
+      "metadata": {
+        "id": "3ci1vysfRdUo"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Training-time regularization for OOD detection"
+      ],
+      "metadata": {
+        "id": "uZn6oCV2aOx9"
+      }
+    }
+  ]
+}

code/conversion-scripts/convert-ipynb-to-md.py

@@ -44,18 +44,18 @@ def process_markdown_cell(cell):
         elif in_objectives:
             if re.match(r'^#', line):
                 in_objectives = False
-                # markdown_lines.append("::::::::::::::::::::::::::::::::::::::: objectives")
-                # markdown_lines.extend(temp_objectives)
-                # markdown_lines.append("::::::::::::::::::::::::::::::::::::::::::::::::::")
+                markdown_lines.append("::::::::::::::::::::::::::::::::::::::: objectives")
+                markdown_lines.extend(temp_objectives)
+                markdown_lines.append("::::::::::::::::::::::::::::::::::::::::::::::::::")
                 # markdown_lines.append(line)
             else:
                 temp_objectives.append(line)
         elif in_questions:
             if re.match(r'^#', line):
                 in_questions = False
-                # markdown_lines.append(":::::::::::::::::::::::::::::::::::::::: questions")
-                # markdown_lines.extend(temp_questions)
-                # markdown_lines.append("::::::::::::::::::::::::::::::::::::::::::::::::::")
+                markdown_lines.append(":::::::::::::::::::::::::::::::::::::::: questions")
+                markdown_lines.extend(temp_questions)
+                markdown_lines.append("::::::::::::::::::::::::::::::::::::::::::::::::::")
                 # markdown_lines.append(line)
             else:
                 temp_questions.append(line)

config.yaml

@@ -66,6 +66,8 @@ episodes:
 - 5d-gradcam.md
 - 6-confidence-intervals.md
 - 7a-OOD-detection-output-based.md
+- 7b-OOD-detection-distance-based.md
+- 7c-OOD-detection-algo-design.md
 - 8-releasing-a-model.md
 
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