Final editorial review.

Author: madeline-underwood

content/learning-paths/cross-platform/simd-info-demo/conclusion.md

@@ -12,7 +12,7 @@ Porting SIMD code between architectures can be a daunting process, often requiri
 
 The primary focus of this Learning Path is to optimize the existing algorithm directly with SIMD intrinsics, without altering the algorithm or data layout. While reordering data to align with native Arm instructions can offer performance benefits, this is outside the scope of this Learning Path. 
 
-If you are interested in data layout strategies to further enhance performance on Arm, see the Learning Path *Optimize SIMD code with vectorization-friendly data layout* linked to in the **Next Steps** section at the end. This provides valuable insights.
+If you are interested in data layout strategies to further enhance performance on Arm, see the Learning Path *Optimize SIMD code with vectorization-friendly data layout* linked to in the **Next Steps** section at the of this Learning Path. 
 
 Using SIMD.info can be instrumental in reducing the amount of time spent in this process, providing a centralized and user-friendly resource for finding NEON equivalents to intrinsics of other architectures. It saves considerable time and effort by offering detailed descriptions, prototypes, and comparisons directly, eliminating the need for extensive web searches and manual lookups.