spelling fixes

Author: jasonrandrews

.wordlist.txt

@@ -3215,4 +3215,34 @@ pyplot
 scheduler's
 torchvision
 uOps
-unsqueeze
+unsqueeze
+
+ACR
+Abena
+Adoptium
+Amanfo
+Arlo
+Fitbit
+Milvus
+Amanfo
+OCTLA
+OpenAg
+Sysbox
+TCK
+TOSA
+Temurin
+Zhang
+Zilliz
+arrhythmias
+ggerganov
+milvus
+msbuild
+nestybox
+pte
+replug
+sam
+sysbox
+tinyml
+tvOS
+watchOS
+zilliz

content/install-guides/multipass.md

@@ -60,7 +60,7 @@ Install the download using the package command.
 sudo installer -pkg multipass-1.14.1-rc1+mac.14+gf2381bfe9.mac-Darwin.pkg -target /
 ```
 
-The getting started instructions below use the command line interface. If you perfer to use the graphical interface start it from the macOS Launchpad, the initial screen is shown below. You can use the UI to create, start, and stop virtual machines. 
+The getting started instructions below use the command line interface. If you prefer to use the graphical interface start it from the macOS Launchpad, the initial screen is shown below. You can use the UI to create, start, and stop virtual machines. 
 
 ![Connect #center](/install-guides/_images/multipass-gui.png)
 

content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/inference.md

@@ -16,7 +16,7 @@ The inference process involves using a trained model to make predictions on new,
 
 This process allows the model to generalize its learned knowledge to make accurate predictions on new data.
 
-# Runing inference in PyTorch
+# Running inference in PyTorch
 
 You can inference in PyTorch using the previously saved model. To display results, you can use matplotlib. 
 

content/learning-paths/cross-platform/pytorch-digit-classification-training/inference.md

@@ -16,7 +16,7 @@ The inference process involves using a trained model to make predictions on new,
 
 This process allows the model to generalize its learned knowledge to make accurate predictions on new data.
 
-# Runing inference in PyTorch
+# Running inference in PyTorch
 
 You can inference in PyTorch using the previously saved model. To display results you can use matplotlib. 
 

content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md

@@ -28,7 +28,7 @@ The host machine is where you will perform most of your development work, especi
 
 ## Install Python
 
-Python 3 is included in Ubuntu, but some additonal packages are needed. 
+Python 3 is included in Ubuntu, but some additional packages are needed. 
 
 ```console
 sudo apt update
@@ -37,7 +37,7 @@ sudo apt install python-is-python3 gcc g++ make -y
 
 ## Install PyTorch
 
-Create a Python virtual environemnt using Miniconda. 
+Create a Python virtual environment using Miniconda. 
 
 For Arm Linux:
 
@@ -97,7 +97,7 @@ The Edge Impulse CLI tools require Node.js.
 sudo apt install nodejs npm -y
 ```
 
-Confirm `node` is avilable by running: 
+Confirm `node` is available by running: 
 
 ```console
 node -v 

content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md

@@ -15,4 +15,4 @@ layout: learningpathall
 - Utilize model quantization techniques to optimize performance on constrained devices like the Grove - Vision AI Module V2.
 - Regularly update your development environment and tools to benefit from the latest improvements in TinyML and edge AI technologies
 
-You've now set up your environment for TinyML development, and tested a PyTorch and ExecuTorch Neural Netrowk.
+You've now set up your environment for TinyML development, and tested a PyTorch and ExecuTorch Neural Network.