Merge pull request #322 from dtischler/main

spelling and nav update

.wordlist.txt

@@ -1784,4 +1784,5 @@ SonicSight
 FedAvg
 EfficientNetB
 generalizable
+wakeword
 

README.md

@@ -103,7 +103,7 @@ Audio classification, keyword spotting, wakeword detection, or other machine lea
 
 ### Predictive Maintenance & Fault Classification
 
-Projects devoted to the use of sensors, audio, or image data specfic to the predictive maintenance use-case.
+Projects devoted to the use of sensors, audio, or image data specific to the predictive maintenance use-case.
 
 * [Predictive Maintenance - Nordic Thingy:91](predictive-maintenance-and-fault-classification/predictive-maintenance-with-nordic-thingy91.md)
 * [Brushless DC Motor Anomaly Detection](predictive-maintenance-and-fault-classification/brushless-dc-motor-anomaly-detection.md)
@@ -173,3 +173,4 @@ Using machine learning to predict, understand, or identify information in the ai
 * [Using Hugging Face Image Classification Datasets with Edge Impulse](software-integration-demos/hugging-face-image-classification-dataset.md)
 * [Edge Impulse API Usage Sample Application - Jetson Nano Trainer](software-integration-demos/api-sample-application-jetson-nano.md)
 * [MLOps with Edge Impulse and Azure IoT Edge](software-integration-demos/mlops-azure-iot-edge.md)
+* [A Federated Approach to Train and Deploy Machine Learning Models](federated-learning.md)

SUMMARY.md

@@ -157,3 +157,4 @@
 * [Using Hugging Face Image Classification Datasets with Edge Impulse](software-integration-demos/hugging-face-image-classification-dataset.md)
 * [Edge Impulse API Usage Sample Application - Jetson Nano Trainer](software-integration-demos/api-sample-application-jetson-nano.md)
 * [MLOps with Edge Impulse and Azure IoT Edge](software-integration-demos/mlops-azure-iot-edge.md)
+* [A Federated Approach to Train and Deploy Machine Learning Models](federated-learning.md)

software-integration-demos/federated-learning.md

@@ -175,7 +175,7 @@ Finally, when all the images have been labeled, we can click "Model testing" and
 
 ## Result
 
-Finally, after training a decentralized model and uploading it to Edge Impulse, one incredible feature that we can benefit from is a seamless deployment of the model on hardwares ranging from MCUs, CPUs and custom AI accelerators. In this case, we can deploy our model to the Raspberry Pi as an [.eim executable](https://docs.edgeimpulse.com/docs/tools/edge-impulse-for-linux#.eim-models) that contains the signal processing and ML code, compiled with optimizations for a processor or GPU (e.g. NEON instructions on ARM cores) plus a very simple IPC layer (over a Unix socket).
+Finally, after training a decentralized model and uploading it to Edge Impulse, one incredible feature that we can benefit from is a seamless deployment of the model on hardware ranging from MCUs, CPUs and custom AI accelerators. In this case, we can deploy our model to the Raspberry Pi as an [.eim executable](https://docs.edgeimpulse.com/docs/tools/edge-impulse-for-linux#.eim-models) that contains the signal processing and ML code, compiled with optimizations for a processor or GPU (e.g. NEON instructions on ARM cores) plus a very simple IPC layer (over a Unix socket).
 
 First, we need to attach the Raspberry Pi camera to the to the board.