Merge pull request #294 from dtischler/main

link fixes

brushless-dc-motor-anomaly-detection.md

@@ -92,7 +92,7 @@ Since the commanded motor velocity is constantly changing, so is the motor power
 
 ## Streaming our data to the Edge Impulse studio
 
-Assuming you've gone through the [Getting Started guide](https://docs.edgeimpulse.com/docs) and have [made an account](https://docs.edgeimpulse.com/login?redirect_uri=/docs) with Edge Impulse, go ahead and make a new project.
+Assuming you've gone through the [Getting Started guide](https://docs.edgeimpulse.com/docs) and have [made an account](https://docs.edgeimpulse.com/docs) with Edge Impulse, go ahead and make a new project.
 
 Following the [guide](https://docs.edgeimpulse.com/docs/cli-data-forwarder) for using Edge Impulse's data forwarder we should see our virtual device appear when we click **Data**  **acquisition**:
 

esd-protection-using-computer-vision.md

@@ -22,7 +22,7 @@ Public Project Link:
 
 ![](.gitbook/assets/esd-protection-using-computer-vision/intro.jpg)
 
-As part of the Edge Impulse Expert Network, Seeed Studio graciously provided some [reComputer Jetson-10-1-H0's](https://wiki.seeedstudio.com/reComputer_getting_started/) to develop sample projects and run the hardware through its paces.
+As part of the Edge Impulse Expert Network, Seeed Studio graciously provided some [reComputer Jetson-10-1-H0's](https://wiki.seeedstudio.com/reComputer_Jetson_Series_Initiation/) to develop sample projects and run the hardware through its paces.
 
 The Seeed reComputer uses a Jetson Nano 4GB with 16GB eMMC (more on that later) housed in a really slick case. It already comes loaded with Jetpack 4.6 and Ubuntu 18.04 LTS and is essentially ready to go out of the box.
 

hand-gesture-recgnition-using-tinyml-on-openmv.md

@@ -81,7 +81,7 @@ In this project, we have built a developed recognition model based on a FOMO alg
 ## Reference
 1. [https://www.edgeimpulse.com/](https://www.edgeimpulse.com)
 2. Memo, L. Minto, P. Zanuttigh, "Exploiting Silhouette Descriptors and Synthetic Data for Hand Gesture Recognition", STAG: Smart Tools & Apps for Graphics, 2015
-3. [FOMO: Object detection for constrained devices - Edge Impulse Documentation](https://docs.edgeimpulse.com/docs/tutorials/fomo-object-detection-for-constrained-devices)
+3. [FOMO: Object detection for constrained devices - Edge Impulse Documentation](https://docs.edgeimpulse.com/docs/edge-impulse-studio/learning-blocks/object-detection/fomo-object-detection-for-constrained-devices)
 4. [OpenMV Cam H7 Plus | OpenMV](https://openmv.io/products/openmv-cam-h7-plus)
 5. [https://docs.edgeimpulse.com/docs/tutorials/running-your-impulse-locally/running-your-impulse-openmv](https://docs.edgeimpulse.com/docs/tutorials/running-your-impulse-locally/running-your-impulse-openmv)
 

indoor-co2-level-estimation-using-tinyml.md

@@ -57,7 +57,7 @@ I used this feature to label _people_ in the PIROPO images. I then divided the d
 
 #### **2. Training and Testing**
 
-Training and testing are done using above mentioned PIROPO dataset. I used the [FOMO](https://www.edgeimpulse.com/blog/announcing-fomo-faster-objects-more-objects) architecture by the Edge Impulse to train this model. To prepare a model using FOMO, please follow this [link](https://docs.edgeimpulse.com/docs/tutorials/counting-objects-using-fomo).
+Training and testing are done using above mentioned PIROPO dataset. I used the [FOMO](https://www.edgeimpulse.com/blog/announcing-fomo-faster-objects-more-objects) architecture by the Edge Impulse to train this model. To prepare a model using FOMO, please follow this [link](https://docs.edgeimpulse.com/docs/tutorials/object-detection/detect-objects-using-fomo).
 
 ![Training statistics](.gitbook/assets/training.jpg)