Note fix

benchmarking.qmd

@@ -7,7 +7,7 @@ Benchmarking is a critical part of developing and deploying machine learning sys
 This chapter will provide an overview of popular ML benchmarks, best practices for benchmarking, and how to use benchmarks to improve model development and system performance. It aims to provide developers with the right tools and knowledge to effectively benchmark and optimize their systems, especially for tinyML systems.
 
 ::: {.callout-tip collapse="true"} 
-Learning Objectives
+## Learning Objectives
 
 * Understand the purpose and goals of benchmarking AI systems, including performance assessment, resource evaluation, validation, and more.