Chng callout-note to callout-tip for learnign objs

harvard-edge · Oct 11, 2023 · c50667a · c50667a
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diff --git a/ai_for_good.qmd b/ai_for_good.qmd
@@ -4,7 +4,7 @@ All of this technological discussion around embedded AI and TinyML we have been
 
 > The "AI for Good" movement plays a critical role in cultivating a future where an AI-empowered society is more just, sustainable, and prosperous for all of humanity.
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/benchmarking.qmd b/benchmarking.qmd
@@ -1,6 +1,6 @@
 # Benchmarking AI
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/case_studies.qmd b/case_studies.qmd
@@ -1,6 +1,6 @@
 # Case Studies
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/dl_primer.qmd b/dl_primer.qmd
@@ -2,7 +2,7 @@
 
 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"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * Understand the basic concepts and definitions of deep neural networks.

diff --git a/efficient_ai.qmd b/efficient_ai.qmd
@@ -1,6 +1,6 @@
 # Efficient AI
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/embedded_ml.qmd b/embedded_ml.qmd
@@ -2,7 +2,7 @@
 
 Before delving into the intricacies of TinyML, it's crucial to grasp the distinctions among Cloud ML, Edge ML, and TinyML. In this chapter, we'll explore each of these facets individually before comparing and contrasting them.
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * Compare Cloud ML, Edge ML, and TinyML in terms of processing location, latency, privacy, computational power, etc.
@@ -253,7 +253,7 @@ The embedded ML landscape is in a state of rapid evolution, poised to enable int
 
 Now would be a great time for you to try out a small computer vision model out of the box.
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Nicla Vision
 
 If you want to play with an embedded system, try out the Nicla Vision

diff --git a/embedded_sys.qmd b/embedded_sys.qmd
@@ -4,7 +4,7 @@ In the domain of TinyML, embedded systems serve as the bedrock, providing a robu
 
 As we journey further into this chapter, we will demystify the intricate yet captivating realm of embedded systems, gaining insights into their structural design, operational features, and the crucial part they play in enabling TinyML applications. From an introduction to the fundamentals of microcontroller units to a deep dive into the interfaces and peripherals that amplify their capabilities, this chapter aims to be a comprehensive guide for understanding the nuanced aspects of embedded systems within the TinyML landscape.
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * Understand the definition, characteristics, history, and importance of embedded systems, especially in relation to tinyML. 
@@ -382,7 +382,7 @@ As we gaze into the future, it's clear that the realm of embedded systems stands
 
 Now would be a great time for you to get your hands on a real embedded device, and get it setup. 
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Nicla Vision
 
 If you want to play with an embedded system, try out the Nicla Vision

diff --git a/ethics.qmd b/ethics.qmd
@@ -1,6 +1,6 @@
 # Ethical AI
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/frameworks.qmd b/frameworks.qmd
@@ -1,6 +1,6 @@
 # AI Frameworks
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/generative_ai.qmd b/generative_ai.qmd
@@ -1,6 +1,6 @@
 # Generative AI
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/hw_acceleration.qmd b/hw_acceleration.qmd
@@ -1,6 +1,6 @@
 # AI Acceleration 
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/ondevice_learning.qmd b/ondevice_learning.qmd
@@ -1,6 +1,6 @@
 # On-Device Learning
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/ops.qmd b/ops.qmd
@@ -1,6 +1,6 @@
 # Embedded AIOps
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/optimizations.qmd b/optimizations.qmd
@@ -1,6 +1,6 @@
 # Model Optimizations
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/privacy_security.qmd b/privacy_security.qmd
@@ -1,6 +1,6 @@
 # Privacy and Security
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/responsible_ai.qmd b/responsible_ai.qmd
@@ -1,6 +1,6 @@
 # Responsible AI
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/training.qmd b/training.qmd
@@ -1,6 +1,6 @@
 # AI Training
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * coming soon.

diff --git a/workflow.qmd b/workflow.qmd
@@ -4,7 +4,7 @@ In this chapter, we'll explore the machine learning (ML) workflow, setting the s
 
 The ML workflow is a structured approach that guides professionals and researchers through the process of developing, deploying, and maintaining ML models. This workflow is generally divided into several crucial stages, each contributing to the effective development of intelligent systems.
 
-::: {.callout-note collapse="true"}
+::: {.callout-tip collapse="true"}
 ## Learning Objectives
 
 * Understand the ML workflow and gain insights into the structured approach and stages involved in developing, deploying, and maintaining machine learning models.