Merge pull request #541 from HyunjunA/master

Update explanation in the "What is AliroEd?"
EpistasisLab · Jan 16, 2023 · 98e8e2d · 98e8e2d
2 parents 74c6a6d + 49b774e
commit 98e8e2d
Showing 1 changed file with 23 additions and 12 deletions.
diff --git a/raspberrypi/intropage/index.html b/raspberrypi/intropage/index.html
@@ -198,15 +198,12 @@ <h1 class="fw-light text-white">What is AliroEd?</h1>
               class="lead text-white"
               style="padding-left: 50px; padding-right: 50px"
             >
-              AliroEd is a software for users who are not familiar with machine
-              learning. Users can experience machine learning tasks on Raspberry
-              Pi 4 with AliroEd. Users can download the raspberry pi image from
-              the AliroEd website. Users can easily install and run AliroEd on
-              Raspberry Pi 4 using this image. AliroEd and the Raspberry Pi
-              image provided in this website, are developed by the AI Research
-              Center at the Computational Biomedicine Department Cedars-Sinai
-              Medical Center, Los Angeles, CA, USA. Please click the following
-              button for more information.
+              AliroEd is a software package running with Raspberry Pi 400 for
+              users who are new to machine learning and AI. Users can experience
+              machine learning tasks on Raspberry Pi 400 with AliroEd. Users can
+              download the AliroEd-RPI-image from the AliroEd website. Users can
+              easily install and run the AliroEd on Raspberry Pi 400 using this
+              image.
             </p>
             <p>
               <!-- <a id='infanddownload' href="./infAndDownloadpage.html" class="btn btn-primary my-2">Download</a> -->
@@ -230,9 +227,23 @@ <h1 class="fw-light text-white">
               class="lead text-white"
               style="padding-left: 50px; padding-right: 50px"
             >
-              If you are interested in getting intuitions about machine
-              learning, please click the below button in the card to see the
-              visual explanation of decision tree.
+              If you are interested in learning about machine learning
+              visualization, please click the below button in the card to see
+              the visual explanation of decision tree. In the visual
+              explanation, with the iris dataset, you can see what features are
+              and how decision tree classifies the data points as one of the
+              three classes (setosa, versicolor, virginica), based on the
+              features. The dataset can be found at
+              <a
+                style="color: #000000; font-weight: bold"
+                href="https://github.com/EpistasisLab/pmlb/tree/master/datasets"
+                >Penn Machine Learning Benchmarks</a
+              >. The source code is in
+              <a
+                href="https://github.com/EpistasisLab/Aliro"
+                style="color: #000000; font-weight: bold"
+                >the github repository.</a
+              >
             </p>
             <!-- <p>
           <a id='loadingbutton' href="#" class="btn btn-primary my-2">Loading AliroEd ...</a>