From c50667a5fa4a02827ae2d5e2ae76dc0c66e3b8c6 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Tue, 10 Oct 2023 20:30:27 -0400 Subject: [PATCH] Chng callout-note to callout-tip for learnign objs --- ai_for_good.qmd | 2 +- benchmarking.qmd | 2 +- case_studies.qmd | 2 +- dl_primer.qmd | 2 +- efficient_ai.qmd | 2 +- embedded_ml.qmd | 4 ++-- embedded_sys.qmd | 4 ++-- ethics.qmd | 2 +- frameworks.qmd | 2 +- generative_ai.qmd | 2 +- hw_acceleration.qmd | 2 +- ondevice_learning.qmd | 2 +- ops.qmd | 2 +- optimizations.qmd | 2 +- privacy_security.qmd | 2 +- responsible_ai.qmd | 2 +- training.qmd | 2 +- workflow.qmd | 2 +- 18 files changed, 20 insertions(+), 20 deletions(-) diff --git a/ai_for_good.qmd b/ai_for_good.qmd index e54250b2..3a6886a0 100644 --- 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 index 5deeb418..5e1a634b 100644 --- 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 index 0e60e006..7f5c276b 100644 --- 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 index 7af15ae4..3ee20490 100644 --- 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 index 5120a8e3..84dcfee6 100644 --- 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 index f6f96a88..70ad6b02 100644 --- 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 index 9e7b88ee..da6632a4 100644 --- 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 index 87bdc4d5..ea57f163 100644 --- 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 index d1bf5210..85f2ab9b 100644 --- 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 index 5ea2191a..1ed91431 100644 --- 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 index f99377bf..46d474ca 100644 --- 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 index 09c821ed..cc587117 100644 --- 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 index 9b43ce1b..9b60f7df 100644 --- 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 index e2ec5ca3..f3e1746e 100644 --- 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 index a0b4552f..03756d87 100644 --- 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 index 5e00580a..6fdaed16 100644 --- 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 index 2d87a779..1027a9c0 100644 --- 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 index 7d7f7360..3c4d3c22 100644 --- 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.