description update

accelerometer-and-activity-projects/arduino-kway-fall-detection.md

@@ -1,7 +1,7 @@
 ---
 description: >-
   Train a TinyML model to detect the motion of falling down, then connect via
-  Bluetooth to make an emergency call
+  Bluetooth to make an emergency call.
 ---
 
 # Arduino x K-Way - TinyML Fall Detection

accelerometer-and-activity-projects/arduino-kway-gesture-recognition-weather.md

@@ -1,7 +1,7 @@
 ---
 description: >-
   Use a Nicla Sense ME attached to the sleeve of a K-way jacket for gesture
-  recognition and bad weather prediction
+  recognition and bad weather prediction.
 ---
 
 # Arduino x K-Way - Gesture Recognition for Hiking

accelerometer-and-activity-projects/hospital-bed-occupancy-detection-arduino-nano-33.md

@@ -4,7 +4,7 @@ description: >-
   hospitals or care facilities.
 ---
 
-# Hospital Bed Occupancy Detetction with TinyML
+# Hospital Bed Occupancy Detection with TinyML
 
 Created By: [Adam Milton-Barker](https://www.adammiltonbarker.com/)
 

audio-projects/glass-break-detection-nordic-thingy53.md

@@ -1,6 +1,6 @@
 ---
 description: >-
-  Build a machine learning model and deploy it to a Nordic Semi Thingy:53 to
+  Build a machine learning model and deploy it to a Nordic Thingy:53 to
   detect the sound of breaking glass.
 ---
 

audio-projects/smart-appliance-voice-commands-nordic-thingy53.md

@@ -1,6 +1,6 @@
 ---
 description: >-
-  Using a Nordic Semi Thingy:53 with Keyword Spotting to turn an ordinary device
+  Using a Nordic Thingy:53 with Keyword Spotting to turn an ordinary device
   into a smart appliance.
 ---
 

audio-projects/wearable-cough-sensor-arduino-nano-33.md

@@ -1,6 +1,6 @@
 ---
 description: >-
-  An exploration into using Machine Learning to better monitor a patient
+  An exploration into using machine learning to better monitor a patient
   coughing, to improve medical outcomes.
 ---
 

image-projects/nvidia-omniverse-replicator.md

@@ -1,7 +1,7 @@
 ---
 description: >-
   Learn how to generate photorealistic images in Nvidia Omniverse Replicator and
-  build an object detection model using Edge Impulse
+  build an object detection model using Edge Impulse.
 ---
 
 # Creating Synthetic Data with Nvidia Omniverse Replicator

predictive-maintenance-and-fault-classification/brushless-dc-motor-anomaly-detection.md

@@ -131,7 +131,7 @@ Now we're ready to apply the signal processing to our data. In the "**Generate f
 
 We're ready to move on to the next block where we create our machine learning model. We're almost done! Once we've generated the DSP features we can navigate to the next screen "Anomaly detection" from the menu on the left.
 
-On this screen we can set the number ofclusters, as well as select the axes according to which our data will be clustered. For this example all axes were selected, but if you know that certain axes are more / less important it's best to select them accordingly _(this can be determined by using samples where the motor is experiencing faulty behavior and using the_ _**Calculate feature importance**_ \_option in the Generate features section. More on this [here](https://www.edgeimpulse.com/blog/advanced-anomaly-detection-with-feature-importance).)
+On this screen we can set the number of clusters, as well as select the axes according to which our data will be clustered. For this example all axes were selected, but if you know that certain axes are more / less important it's best to select them accordingly _(this can be determined by using samples where the motor is experiencing faulty behavior and using the_ _**Calculate feature importance**_ \_option in the Generate features section. More on this [here](https://www.edgeimpulse.com/blog/advanced-anomaly-detection-with-feature-importance).)
 
 ![](https://hackster.imgix.net/uploads/attachments/1444526/image\_iPSTpWYYod.png?auto=compress,format\&w=740\&h=555\&fit=max)
 

software-integration-demos/mlops-azure-iot-edge.md

@@ -1,3 +1,8 @@
+---
+description: >-
+  Use Docker containers distributed via Azure IoT Edge to build and deploy machine leaning models in an MLOps loop.
+---
+
 # MLOps with Edge Impulse and Azure IoT Edge
 
 Created By: David Tischler

software-integration-demos/ros2-part1-pubsub-node.md

@@ -1,8 +1,6 @@
 ---
 description: >-
-  In this tutorial we’ll look at how to build an AI-driven ROS2 node using an
-  Edge Impulse model. This tutorial is “sensor agnostic”, but a 3-axis
-  accelerometer is used for demonstration.
+  Build an AI-driven ROS2 node for robotics using an Edge Impulse model and a 3-axis accelerometer.
 ---
 
 # ROS2 + Edge Impulse, Part 1: Pub/Sub Node in Python
@@ -11,9 +9,9 @@ Created By: Avi Brown
 
 Public Project Link: [https://studio.edgeimpulse.com/public/108508/latest](https://studio.edgeimpulse.com/public/108508/latest)
 
-{% embed url="https://www.youtube.com/watch?v=0SabLvJqSaM" %}
+GitHub Repository: [https://github.com/avielbr/edge-impulse/tree/main/ros2/ei\_ros2](https://github.com/avielbr/edge-impulse/tree/main/ros2/ei\_ros2)
 
-### Full code for this project can be [found here](https://github.com/avielbr/edge-impulse/tree/main/ros2/ei\_ros2)
+{% embed url="https://www.youtube.com/watch?v=0SabLvJqSaM" %}
 
 ### Background