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 # Deep Learning Primer
 
+This section offers a brief introduction to deep learning, starting with an overview of its history, applications, and relevance to embedded AI systems. It examines the core concepts like neural networks, highlighting key components like perceptrons, multilayer perceptrons, activation functions, and computational graphs. The primer also briefly explores major deep learning architecture, contrasting their applications and uses. Additionally, it compares deep learning to traditional machine learning to equip readers with the general conceptual building blocks to make informed choices between deep learning and traditional ML techniques based on problem constraints, setting the stage for more advanced techniques and applications that will follow in subsequent chapters.
+
 ::: {.callout-note collapse="true"}
 ## Learning Objectives
 
-* coming soon.
+* Understand the basic concepts and definitions of deep neural networks.
+
+* Recognize there are different deep learning model architectures.
 
+* Comparison between deep learning and traditional machine learning approaches across various dimensions.
+
+* Acquire the basic conceptual building blocks to delve deeper into advanced deep learning techniques and applications.
+  
 :::
 
 ## Introduction