Merge pull request #1828 from jasonrandrews/spelling

Spelling updates

Author: jasonrandrews

.wordlist.txt

@@ -3879,7 +3879,6 @@ DLRMv
 DeepSeek
 Geremy
 MERCHANTABILITY
-MLPerf’s
 MoE
 NONINFRINGEMENT
 NaN
@@ -3928,7 +3927,6 @@ HelloworldSubscriber
 IMU
 Jalisco
 LiDAR
-MLPerf’s
 OTA
 OpenAD
 OpenADKit
@@ -3958,4 +3956,14 @@ ros
 rviz
 testbed
 ug
-vnc
+vnc
+Acyclic
+Bedrust
+MLPerf's
+bedrust
+darko
+mesaros
+multilayer
+renderbuffer
+rosdep
+suboptimally

content/learning-paths/cross-platform/llm-fine-tuning-for-web-applications/how-to-6.md

@@ -31,7 +31,7 @@ This section runs LLM inference using the fine-tuned model.
 
     Use the command below to:
     - Optimize the model for low-latency inference.
-    - Use Unsloth’s performance improvements to speed up text generation.checkpoints in "outputs" folder.
+    - Use Unsloth's performance improvements to speed up text generation.checkpoints in "outputs" folder.
 
     ```python
     FastLanguageModel.for_inference(model)

content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/generating-a-render-graph-for-your-application.md

@@ -47,7 +47,7 @@ Ask Frame Advisor to capture data relating to the problem areas you have observe
 
 ## Viewing the render graph
 
-Observe that part of the Frame Advisor window is labelled “Render Graph”. This contains the render graph relating to the frames you asked Frame Advisor to analyze.
+Observe that part of the Frame Advisor window is labeled “Render Graph”. This contains the render graph relating to the frames you asked Frame Advisor to analyze.
 
 Assume that you've captured the following render graph:
 

content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/inefficient-transfer-workloads.md

@@ -25,7 +25,7 @@ To find which API calls your application uses to start transfer workloads:
 
 - Open the render graph for your captured frames
 - Click a transfer node
-- Now move to the API Calls view (labelled “API Calls”)
+- Now move to the API Calls view (labeled “API Calls”)
 - Observe the API calls in use.
 
 ## Problem: inefficient clear routines

content/learning-paths/mobile-graphics-and-gaming/render-graph-optimization/understanding-your-render-graph.md

@@ -35,7 +35,7 @@ Take a closer look at what can be represented in a node on the render graph.
 
 ### Render passes
 
-Most of the execution nodes in the sample graph above are colored green and labelled “RP…” These are [render passes](https://developer.arm.com/documentation/102479/0100/How-Render-Passes-Work).
+Most of the execution nodes in the sample graph above are colored green and labeled “RP…” These are [render passes](https://developer.arm.com/documentation/102479/0100/How-Render-Passes-Work).
 
 ![A render pass node#center](render-pass-node.png "Figure 2. A render pass node")
 
@@ -53,7 +53,7 @@ When you click an execution node, such as a render pass, Frame Advisor navigates
 
 ### Other types of execution node
 
-The graph also shows a transfer node, colored blue, and labelled ”Tr…”.
+The graph also shows a transfer node, colored blue, and labeled ”Tr…”.
 
 ![A transfer node#center](transfer-node.png "A transfer node")
 

content/learning-paths/servers-and-cloud-computing/dlrm/1-overview.md

@@ -18,7 +18,7 @@ The Arm Neoverse V2 CPU is built for high-performance computing, making it ideal
 
 In this Learning Path, you'll learn how to evaluate the performance of the [DLRM using the MLPerf Inference suite](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm_v2/pytorch) in the _Offline_ scenario. The Offline scenario is a test scenario where large batches of data are processed all at once, rather than in real-time. It simulates large-scale, batch-style inference tasks commonly found in recommendation systems for e-commerce, streaming, and social platforms.
 
-You will run tests that measure throughput (samples per second) and latency, providing insights into how efficiently the model runs on the target system. By using MLPerf’s standardized methodology, you'll gain reliable insights that help compare performance across different hardware and software configurations — highlighting the system’s ability to handle real-world, data-intensive AI workloads.
+You will run tests that measure throughput (samples per second) and latency, providing insights into how efficiently the model runs on the target system. By using MLPerf's standardized methodology, you'll gain reliable insights that help compare performance across different hardware and software configurations — highlighting the system’s ability to handle real-world, data-intensive AI workloads.
 
 ## Configure your environment