Updated learning objectives

frameworks.qmd

@@ -1,19 +1,25 @@
 # AI Frameworks
 
+In this chapter, we explore the landscape of AI frameworks that serve as the foundation for developing machine learning systems. AI frameworks provide the essential tools, libraries, and environments necessary to design, train, and deploy machine learning models. We delve into the evolutionary trajectory of these frameworks, dissect the workings of TensorFlow,  and provide insights into the core components and advanced features that define these frameworks. 
+
+Furthermore, we investigate the specialization of frameworks tailored to specific needs, the emergence of frameworks specifically designed for embedded AI, and the criteria for selecting the most suitable framework for your project. This exploration will be rounded off by a glimpse into the future trends that are expected to shape the landscape of ML frameworks in the coming years.
+
 ::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
-* The evolution, core components, and advanced features of ML frameworks
+* Understand the evolution and capabilities of major machine learning frameworks. This includes graph execution models, programming paradigms, hardware acceleration support, and how they have expanded over time.
+
+* Learn the core components and functionality of frameworks like computational graphs, data pipelines, optimization algorithms, training loops, etc. that enable efficient model building.
 
-* How frameworks specialize for cloud, edge, and tinyML environments
+* Compare frameworks across different environments like cloud, edge, and tinyML. Learn how frameworks specialize based on computational constraints and hardware.
 
-* Challenges of embedded ML and how frameworks optimize models
+* Dive deeper into embedded and tinyML focused frameworks like TensorFlow Lite Micro, CMSIS-NN, TinyEngine etc. and how they optimize for microcontrollers. 
 
-* Criteria for selecting the right framework based on models, hardware, software factors
+* Explore model conversion and deployment considerations when choosing a framework, including aspects like latency, memory usage, and hardware support.
 
-* How to match framework capabilities to the constraints and requirements of a project
+* Evaluate key factors in selecting the right framework like performance, hardware compatibility, community support, ease of use, etc. based on the specific project needs and constraints.
 
-* Ongoing innovations in frameworks for next-generation machine learning
+* Understand the limitations of current frameworks and potential future trends like using ML to improve frameworks, decomposed ML systems, and high performance compilers.
 
 :::