Spelling fixes (#164)

* Adding content

* Update en.json

* Update README.md

* Update TRANSLATIONS.md

* Adding lesson tempolates

* Fixing code files with each others code in

* Update README.md

* Adding lesson 16

* Adding virtual camera

* Adding Wio Terminal camera capture

* Adding wio terminal code

* Adding SBC classification to lesson 16

* Adding challenge, review and assignment

* Adding images and using new Azure icons

* Update README.md

* Update iot-reference-architecture.png

* Adding structure for JulyOT links

* Removing icons

* Sketchnotes!

* Create lesson-1.png

* Starting on lesson 18

* Updated sketch

* Adding virtual distance sensor

* Adding Wio Terminal image classification

* Update README.md

* Adding structure for project 6 and wio terminal distance sensor

* Adding some of the smart timer stuff

* Updating sketchnotes

* Adding virtual device speech to text

* Adding chapter 21

* Language tweaks

* Lesson 22 stuff

* Update en.json

* Bumping seeed libraries

* Adding functions lab to lesson 22

* Almost done with LUIS

* Update README.md

* Reverting sunlight sensor change

Fixes #88

* Structure

* Adding speech to text lab for Pi

* Adding virtual device text to speech lab

* Finishing lesson 23

* Clarifying privacy

Fixes #99

* Update README.md

* Update hardware.md

* Update README.md

* Fixing some code samples that were wrong

* Adding more on translation

* Adding more on translator

* Update README.md

* Update README.md

* Adding public access to the container

* First part of retail object detection

* More on stock lesson

* Tweaks to maps lesson

* Update README.md

* Update pi-sensor.md

* IoT Edge install stuffs

* Notes on consumer groups and not running the event monitor at the same time

* Assignment for object detector

* Memory notes for speech to text

* Migrating LUIS to an HTTP trigger

* Adding Wio Terminal speech to text

* Changing smart timer to functions from hub

* Changing a param to body to avoid URL encoding

* Update README.md

* Tweaks before IoT Show

* Adding sketchnote links

* Adding object detection labs

* Adding more on object detection

* More on stock detection

* Finishing stock counting

* Tidying stuff

* Adding wio purchase link

* Updating Seeed logo

* Update pi-proximity.md

* Fix clean up link

Fixes #145

* Moving attributions to a separate file

* First draft of edge classifier

* Adding extras

* Moving folder

* Adding lesson 11 questions

* Image improvements

* More image tweaks

* Adding lesson 12 quiz

* Quiz for lesson 13

* Adding quiz for lesson 14

* Lesson 15 and 16 quiz

* Update README.md

5-retail/lessons/1-train-stock-detector/README.md

@@ -105,7 +105,7 @@ You can train an object detector using Custom Vision, in a similar way to how yo
 
     Call your project `stock-detector`.
 
-    When you create your project, make sure to use the `stock-detector-training` resource you created earlier. Use *Object Detection* project type, and the *Products on Shelves* domain.
+    When you create your project, make sure to use the `stock-detector-training` resource you created earlier. Use the *Object Detection* project type, and the *Products on Shelves* domain.
 
     ![The settings for the custom vision project with the name set to fruit-quality-detector, no description, the resource set to fruit-quality-detector-training, the project type set to classification, the classification types set to multi class and the domains set to food](../../../images/custom-vision-create-object-detector-project.png)
 
@@ -135,7 +135,7 @@ To train your model you will need a set of images containing the objects you wan
 
     ![Tagging some tomato paste](../../../images/object-detector-tag-tomato-paste.png)
 
-    > 💁 If you have more than 15 images for each object, you can train after 15 then use the **Suggested tags** feature. This will use the trained model to detect the objecs in the untagged image. You can then confirm the detected objects, or reject and re-draw the bounding boxes. This can save a *lot* of time.
+    > 💁 If you have more than 15 images for each object, you can train after 15 then use the **Suggested tags** feature. This will use the trained model to detect the objects in the untagged image. You can then confirm the detected objects, or reject and re-draw the bounding boxes. This can save a *lot* of time.
 
 1. Follow the [Train the detector section of the Build an object detector quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/get-started-build-detector?WT.mc_id=academic-17441-jabenn#train-the-detector) to train the object detector on your tagged images.