Merge branch 'main' of https://github.com/pykale/mmai-tutorials

Author: haipinglu

overview.md

@@ -1,6 +1,6 @@
 # Overview
 
-This [Jupyter Book](https://jupyterbook.org) presents tutorials on multimodal AI applications using the `PyKale` library.
+This [Jupyter Book](https://jupyterbook.org) presents tutorials on multimodal AI applications using the [`PyKale`](https://github.com/pykale/pykale) library.
 
 [//]: # (:::{note})
 

tutorials/cardiac-abnormality-assessment/extend-reading/extension-tasks.md

@@ -13,4 +13,4 @@ Zoom into specific regions of the input ECG for detailed model interpretation. Y
 
 Download the [MIMIC-CXR](https://physionet.org/content/mimic-cxr/2.1.0/) and [MIMIC-IV-ECG](https://physionet.org/content/mimic-iv-ecg/1.0/) datasets, then **uncomment** the optional code cell in *[Step 1: Data Loading and Preparation](https://pykale.github.io/mmai-tutorials/tutorials/cardiac-abnormality-assessment/tutorial-heart.html#step-1-data-loading-and-preparation)*.
 
-Set `cfg_PT.TRAIN.LATENT_DIM=128`, `cfg_PT.DATA.BATCH_SIZE=128` and `cfg_PT.TRAIN.EPOCH=100` to obtain the **optimal pre-trained CardioVAE model** using the full 50K paired CXR-ECG data. Compare the results with the pre-trained CardioVAE model using 10K paired CXR-ECG data in the tutorial.
+Set `cfg_PT.TRAIN.LATENT_DIM=128`, `cfg_PT.DATA.BATCH_SIZE=128` and `cfg_PT.TRAIN.EPOCH=100` to obtain the **optimal pre-trained CardioVAE model** using the full 50K paired CXR-ECG data. Compare the results with the pre-trained CardioVAE model using 1K paired CXR-ECG data in the tutorial.