add the CNN notebooks to toc

book/_toc.yml

@@ -3,10 +3,18 @@
 
 format: jb-book
 root: intro
-chapters:
-  - file: notebooks/IO_Zarr.md
-    title: Indian Ocean dataset
-  - file: notebooks/background.md
-    title: Background
-  - file: notebooks/IO_Zarr_visualizations.ipynb
-    title: Data visualizations
+parts:
+  - caption: Data
+    chapters:
+      - file: notebooks/IO_Zarr.md
+        title: Indian Ocean dataset
+      - file: notebooks/background.md
+        title: Background
+      - file: notebooks/IO_Zarr_visualizations.ipynb
+        title: Data visualizations
+  - caption: Models
+    chapters:
+      - file: notebooks/CHL_prediction_CNN.ipynb
+        title: CNNs
+      - file: notebooks/CHL_prediction_ConvLSTM_.ipynb
+        title: ConvLSTM

book/notebooks/CHL_prediction_CNN.ipynb

@@ -1,5 +1,16 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gap-filling with CNN\n",
+    "\n",
+    "**Author:** Yifei Hang (UW)\n",
+    "\n",
+    "This notebook shows how to fit a basic Convolutional Neural Network for filling the gaps in the Chlorophyll-a data. Although you can run this tutorial on CPU, it will be much faster on GPU. We used the image `quay.io/pangeo/ml-notebook:2024.08.18` for running the notebook."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -3019,7 +3030,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "tf_2.10_py_3.9",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -3033,9 +3044,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }