Merge pull request #2479 from jasonrandrews/spelling

Spelling and tag updates

Author: jasonrandrews

.wordlist.txt

@@ -197,7 +197,7 @@ defined in ``misc.c``.
 
 The ``sme2_check`` program then displays whether SVE, SME and SME2 are supported
 at line 24. The checking of SVE, SME and SME2 is done differently depending on
-``BAREMETAL``. This platform specific behaviour is abstracted by the
+``BAREMETAL``. This platform specific behavior is abstracted by the
 ``display_cpu_features()``:
 - In baremetal mode, our program has access to system registers and can inspect system registers for SME2 support. The program will print the SVE field of the ``ID_AA64PFR0_EL1`` system register and the SME field of the ``ID_AA64PFR1_EL1`` system register.
 - In non baremetal mode, on an Apple platform the program needs to use a higher

content/learning-paths/cross-platform/multiplying-matrices-with-sme2/2-check-your-environment.md

@@ -281,7 +281,7 @@ This seems counter-intuitive but the reason is that, unlike C, Rust treats the i
 
 Like functions, inlining them is not always guaranteed. If it is possible to inline the intrinsic, code generation and performance would be almost as that with C. If it is not possible, you might find that the same code in Rust performs worse than in C.
 
-Because of this, you have to look carefully at the disassembly generated from your SIMD Rust code. So, how can you fix this behaviour and get the expected generated code?
+Because of this, you have to look carefully at the disassembly generated from your SIMD Rust code. So, how can you fix this behavior and get the expected generated code?
 
 As you have seen, Rust has a very particular way to enable target features. In this case, you have to remember to add that `dotprod` is the required target feature. Make this change in the function `sad_vec_asimd` as shown below:
 

content/learning-paths/cross-platform/simd-on-rust/simd-on-rust-part2.md

@@ -11,7 +11,7 @@ maintopic: true
 operatingsystems_filter:
 - Android: 1
 - Baremetal: 30
-- Linux: 30
+- Linux: 31
 - macOS: 7
 - RTOS: 9
 - Windows: 4
@@ -20,7 +20,7 @@ subjects_filter:
 - Containers and Virtualization: 6
 - Embedded Linux: 4
 - Libraries: 3
-- ML: 16
+- ML: 17
 - Performance and Architecture: 21
 - RTOS Fundamentals: 4
 - Security: 2
@@ -50,8 +50,8 @@ tools_software_languages_filter:
 - DetectNet: 1
 - Docker: 10
 - DSTREAM: 2
-- Edge AI: 2
-- Edge Impulse: 1
+- Edge AI: 3
+- Edge Impulse: 2
 - ExecuTorch: 4
 - FastAPI: 1
 - FPGA: 1

content/learning-paths/embedded-and-microcontrollers/_index.md

@@ -1,19 +1,19 @@
 ---
-title: Deploy Edge AI models scalably using Edge Impulse and AWS IoT Greengrass
+title: Deploy Edge AI models using Edge Impulse and AWS IoT Greengrass
 
 draft: true
 cascade:
     draft: true
 
 minutes_to_complete: 120
 
-who_is_this_for: This learning path is for Edge AI and embedded engineers who need to scalably deploy crafted ML for the Edge to thousands of edge devices. 
+who_is_this_for: This learning path is for Edge AI and embedded engineers who need to deploy crafted ML for the Edge to thousands of edge devices. 
 
 learning_objectives:
   - Basic understanding of Edge Impulses Edge ML Solution
   - Basic hardware setup for Edge AI ML development with Edge Impulse
   - Install AWS IoT Greengrass onto the edge device
-  - Configure the edge device with the custom integration between Edge Implulse and AWS IoT Greengrass
+  - Configure the edge device with the custom integration between Edge Impulse and AWS IoT Greengrass
 
 prerequisites:
   - An [Edge Impulse Studio](https://studio.edgeimpulse.com/signup) account (workshop will walk through this).

content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/_index.md

@@ -70,7 +70,7 @@ Key in this is the "Impulse".  On the left side of the dashboard, our "Impulse"
 
 ![Edge Impulse](./images/EI_Project_2.png)
 
-Clicking on "Object Detection" on the left, you will see some detail on the model that has been utlized in our Impulse:
+Clicking on "Object Detection" on the left, you will see some detail on the model that has been utilized in our Impulse:
 
 ![Edge Impulse](./images/EI_Project_3.png)
 

content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/edgeimpulseprojectbuild.md

@@ -22,7 +22,7 @@ Prior to installing AWS IoT Greengrass, we need to create a set of AWS credentia
 >		export AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID>
 >		export AWS_SECRET_ACCESS_KEY=<AWS_SECRET_ACCESS_KEY>
 
-If you are using your personal AWS account and do not have the credentails created, you will need to create them. If you already have them, please skip the next step and proceed to step 3) below. 
+If you are using your personal AWS account and do not have the credentials created, you will need to create them. If you already have them, please skip the next step and proceed to step 3) below. 
 
 #### 1a. Creating Access Credentials (personal AWS Accounts)
 

content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/greengrassinstallation.md

@@ -33,7 +33,7 @@ Press "Add security group rule" and lets allow port tcp/4912:
 
 Lets also give the EC2 instance a bit more disk space. Please change the "8" to "28" here:
 
-![Increase Diskspace](../images/EC2_Setup_5.png)
+![Increase disk space](../images/EC2_Setup_5.png)
 
 Finally, press "Launch instance". You should see your EC2 instance getting created:
 

content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/hardware/hardwaresetupec2.md

@@ -20,7 +20,7 @@ So, for example, if my public ip address of my edge device is "1.1.1.1", my url
 
 		http://1.1.1.1:4912
 		
-You should now see both the imput (video either from file or from your edge devices attached camera) as well as inference results and inference times. There are two output scenarios depending on whether your edge device has a camera or does not have a camera... read below!
+You should now see both the input (video either from file or from your edge devices attached camera) as well as inference results and inference times. There are two output scenarios depending on whether your edge device has a camera or does not have a camera... read below!
 
 ### Option 1: Edge devices with cameras
 
@@ -60,7 +60,7 @@ Additionally, you will see, model metrics being published periodically:
 
 ![Model Metrics](./images/EI_Model_Metrics.png)
 
-#### Issuing a command and examing the command result
+#### Issuing a command and examining the command result
 
 The integration provides a set of commands (see the [Summary](8_Summary.md) for details on the commands). One command, in particular, restarts the Edge Impulse "Runner" service. 
 
@@ -102,7 +102,7 @@ and you should see your inferencing resuming. You should also see more inference
 >**_NOTE:_**
 >For those who have edge devices WITHOUT cameras, your runner will read is input image video and report inferences until the video ends. Once ended, the "Runner" will simply wait for you to issue the above "restart" command to replay the video file. The restart command will cause the Runner to restart and it will once again, play the video file. 
 
-Cool!  Congradulations!  You have completed this workshop!!
+Cool!  Congratulations!  You have completed this workshop!!
 
 #### Supplemental notes
 Below are a few additional notes regarding the component deployment, log files, launch times for some devices:

content/learning-paths/embedded-and-microcontrollers/edge_impulse_greengrass/running.md

@@ -8,7 +8,7 @@ layout: learningpathall
 
 ## Summary 
 
-Congradulations!  You have completed this workshop!  Please select "Next" below to read a bit about cleaning up your AWS environment in order to minimize costs/etc (AWS workshop attendees: this will happen automatically for you)
+Congratulations!  You have completed this workshop!  Please select "Next" below to read a bit about cleaning up your AWS environment in order to minimize costs/etc (AWS workshop attendees: this will happen automatically for you)
 
 ### For More Information