I broke the .callout in a prev. push

contents/ai_for_good/ai_for_good.qmd

@@ -78,7 +78,7 @@ Widespread TinyML applications can help digitize smallholder farms to increase p
 
 With greater investment and integration into rural advisory services, TinyML could transform small-scale agriculture and improve farmers' livelihoods worldwide. The technology effectively brings the benefits of precision agriculture to disconnected regions most in need.
 
-:::{#exr-agri.callout-caution collapse="true"}
+:::{#exr-agri .callout-caution collapse="true"}
 
 ### Crop Yield Modeling
 
@@ -125,7 +125,7 @@ An on-device algorithm for early and timely life-threatening VA detection will i
 
 The champion, GaTech EIC Lab, obtained 0.972 in $F_\beta$ (F1 score with a higher weight to recall), 1.747 ms in latency, and 26.39 kB in memory footprint with a deep neural network. An ICD with an on-device VA detection algorithm was [implanted in a clinical trial](https://youtu.be/vx2gWzAr85A?t=2359).
 
-:::{#exr-hc.callout-caution collapse="true"}
+:::{#exr-hc .callout-caution collapse="true"}
 
 ### Clinical Data:  Unlocking Insights with Named Entity Recognition
 
@@ -253,7 +253,7 @@ These slides are a valuable tool for instructors to deliver lectures and for stu
 
 :::
 
-:::{.callout-caution collapse="false"}
+:::{ .callout-caution collapse="false"}
 #### Exercises 
 
 - @exr-agri

contents/benchmarking/benchmarking.qmd

@@ -135,7 +135,7 @@ These types of microbenchmarks include zooming into very specific operations or
 
 Example: [DeepBench](https://github.com/baidu-research/DeepBench), introduced by Baidu, is a good example of something that assesses the above. DeepBench assesses the performance of basic operations in deep learning models, providing insights into how different hardware platforms handle neural network training and inference.
 
-:::{#exr-cuda.callout-caution collapse="true"}
+:::{#exr-cuda .callout-caution collapse="true"}
 
 ### System Benchmarking - Tensor Operations
 
@@ -449,7 +449,7 @@ Metrics: We will measure the following metrics:
 
 By measuring these metrics, we can assess the performance of the object detection model on the edge device and identify any potential bottlenecks or areas for optimization to enhance real-time processing capabilities.
 
-:::{#exr-perf.callout-caution collapse="true"}
+:::{#exr-perf .callout-caution collapse="true"}
 
 ### Inference Benchmarks - MLPerf
 
@@ -821,7 +821,7 @@ These slides are a valuable tool for instructors to deliver lectures and for stu
 
 :::
 
-:::{.callout-caution collapse="false"}
+:::{ .callout-caution collapse="false"}
 #### Exercises 
 
 To reinforce the concepts covered in this chapter, we have curated a set of exercises that challenge students to apply their knowledge and deepen their understanding. 

contents/data_engineering/data_engineering.qmd

@@ -123,7 +123,7 @@ In this context, using KWS as an example, we can break each of the steps out as
 7. **Iterative Feedback and Refinement:**
     Once a prototype KWS system is developed, it's crucial to test it in real-world scenarios, gather feedback, and iteratively refine the model. This ensures that the system remains aligned with the defined problem and objectives. This is important because the deployment scenarios change over time as things evolve.
 
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+:::{#exr-kws .callout-caution collapse="true"}
 
 ### Keyword Spotting with TensorFlow Lite Micro
 
@@ -174,7 +174,7 @@ Web scraping can yield inconsistent or inaccurate data. For example, the photo i
 
 ![A picture of old traffic lights (1914). Credit: [Vox.](https://www.vox.com/2015/8/5/9097713/when-was-the-first-traffic-light-installed)](images/jpg/1914_traffic.jpeg){#fig-traffic-light}
 
-:::{#exr-ws.callout-caution collapse="true"}
+:::{#exr-ws .callout-caution collapse="true"}
 
 ### Web Scraping
 
@@ -219,7 +219,7 @@ While synthetic data offers numerous advantages, it is essential to use it judic
 
 ![Increasing training data size with synthetic data generation. Credit: [AnyLogic](https://www.anylogic.com/features/artificial-intelligence/synthetic-data/).](images/jpg/synthetic_data.jpg){#fig-synthetic-data}
 
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+:::{#exr-sd .callout-caution collapse="true"}
 
 ### Synthetic Data
 Let us learn about synthetic data generation using Generative Adversarial Networks (GANs) on tabular data. We'll take a hands-on approach, diving into the workings of the CTGAN model and applying it to the Synthea dataset from the healthcare domain. From data preprocessing to model training and evaluation, we'll go step-by-step, learning how to create synthetic data, assess its quality, and unlock the potential of GANs for data augmentation and real-world applications.  
@@ -304,7 +304,7 @@ Maintaining the integrity of the data infrastructure is a continuous endeavor. T
 
 There is a boom in data processing pipelines, commonly found in ML operations toolchains, which we will discuss in the MLOps chapter. Briefly, these include frameworks like MLOps by Google Cloud. It provides methods for automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment, and infrastructure management. Several mechanisms focus on data processing, an integral part of these systems.
 
-:::{#exr-dp.callout-caution collapse="true"}
+:::{#exr-dp .callout-caution collapse="true"}
 
 ### Data Processing
 
@@ -493,7 +493,7 @@ These slides are a valuable tool for instructors to deliver lectures and for stu
 
 :::
 
-:::{.callout-caution collapse="false"}
+:::{ .callout-caution collapse="false"}
 #### Exercises 
 
 To reinforce the concepts covered in this chapter, we have curated a set of exercises that challenge students to apply their knowledge and deepen their understanding. 

contents/dl_primer/dl_primer.qmd

@@ -117,7 +117,7 @@ MLPs are basic deep learning architectures comprising three layers: an input lay
 
 In embedded AI systems, MLPs can function as compact models for simpler tasks like sensor data analysis or basic pattern recognition, where computational resources are limited. Their ability to learn non-linear relationships with relatively less complexity makes them a suitable choice for embedded systems.
 
-:::{.callout-caution #exr-mlp collapse="false"}
+:::{ .callout-caution #exr-mlp collapse="false"}
 
 ##### Multilayer Perceptrons (MLPs)
 
@@ -140,7 +140,7 @@ CNNs are mainly used in image and video recognition tasks. This architecture emp
 
 In embedded AI, CNNs are crucial for image and video recognition tasks, where real-time processing is often needed. They can be optimized for embedded systems using techniques like quantization and pruning to minimize memory usage and computational demands, enabling efficient object detection and facial recognition functionalities in devices with limited computational resources.
 
-:::{.callout-caution #exr-cnn collapse="false"}
+:::{ .callout-caution #exr-cnn collapse="false"}
 
 ### Convolutional Neural Networks (CNNs)
 
@@ -282,7 +282,7 @@ These slides are a valuable tool for instructors to deliver lectures and for stu
 
 :::
 
-:::{.callout-caution collapse="false"}
+:::{ .callout-caution collapse="false"}
 #### Exercises 
 
 To reinforce the concepts covered in this chapter, we have curated a set of exercises that challenge students to apply their knowledge and deepen their understanding.

contents/efficient_ai/efficient_ai.qmd

@@ -204,7 +204,7 @@ These slides are a valuable tool for instructors to deliver lectures and for stu
 - [Continuous Evaluation Challenges for TinyML.](https://docs.google.com/presentation/d/1OuhwH5feIwPivEU6pTDyR3QMs7AFstHLiF_LB8T5qYQ/edit?usp=drive_link&resourcekey=0-DZxIuVBUbJawuFh0AO-Pvw)
 :::
 
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 #### Exercises 
 

contents/frameworks/frameworks.qmd

@@ -120,7 +120,7 @@ The Parameter Server (PS) architecture is a popular design for distributing the
 
 **Computation:** The worker processes, which could be run in parallel, were stateless and purely computational. They processed data and computed gradients without maintaining any state or long-term memory [@li2014communication].
 
-:::{#exr-tfc.callout-caution collapse="true"}
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 ### TensorFlow Core
 
@@ -129,7 +129,7 @@ Let's comprehensively understand core machine learning algorithms using TensorFl
 [![](https://colab.research.google.com/assets/colab-badge.png)](https://colab.research.google.com/drive/15Cyy2H7nT40sGR7TBN5wBvgTd57mVKay#scrollTo=IEeIRxlbx0wY)
 :::
 
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 ### TensorFlow Lite
 
@@ -166,7 +166,7 @@ TensorFlow includes libraries to help users develop and deploy more use-case-spe
 
 One of TensorFlow's biggest advantages is its integration with Keras, though, as we will cover in the next section, Pytorch recently added a Keras integration. Keras is another ML framework built to be extremely user-friendly and, as a result, has a high level of abstraction. We will cover Keras in more depth later in this chapter. However, when discussing its integration with TensorFlow, it was important to note that it was originally built to be backend-agnostic. This means users could abstract away these complexities, offering a cleaner, more intuitive way to define and train models without worrying about compatibility issues with different backends. TensorFlow users had some complaints about the usability and readability of TensorFlow's API, so as TF gained prominence, it integrated Keras as its high-level API. This integration offered major benefits to TensorFlow users since it introduced more intuitive readability and portability of models while still taking advantage of powerful backend features, Google support, and infrastructure to deploy models on various platforms.
 
-:::{#exr-k.callout-caution collapse="true"}
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 ### Exploring Keras: Building, Training, and Evaluating Neural Networks
 
@@ -759,7 +759,7 @@ These slides are a valuable tool for instructors to deliver lectures and for stu
     * [TFLite Micro NN Operations.](https://docs.google.com/presentation/d/1ZwLOLvYbKodNmyuKKGb_gD83NskrvNmnFC0rvGugJlY/edit?usp=drive_link)
 :::
 
-:::{.callout-caution collapse="false"}
+:::{ .callout-caution collapse="false"}
 #### Exercises 
 
 To reinforce the concepts covered in this chapter, we have curated a set of exercises that challenge students to apply their knowledge and deepen their understanding. 

contents/hw_acceleration/hw_acceleration.qmd

@@ -594,7 +594,7 @@ Programming models provide abstractions to map computations and data onto hetero
 
 Key challenges include expressing parallelism, managing memory across devices, and matching algorithms to hardware capabilities. Abstractions must balance portability with allowing hardware customization. Programming models enable developers to harness accelerators without hardware expertise. These details are discussed in the [AI frameworks](../frameworks/frameworks.qmd) section.
 
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 ### Software for AI Hardware - TVM
 
@@ -1021,7 +1021,7 @@ Here is a curated list of resources to support students and instructors in their
 * *Coming soon.*
 :::
 
-:::{.callout-caution collapse="false"}
+:::{ .callout-caution collapse="false"}
 #### Exercises 
 
 * @exr-tvm

contents/ml_systems/ml_systems.qmd

@@ -162,7 +162,7 @@ TinyML excels in low-power and resource-constrained settings. These environments
 
 ![Examples of TinyML device kits. Credit: [Widening Access to Applied Machine Learning with TinyML.](https://arxiv.org/pdf/2106.04008.pdf)](images/jpg/tiny_ml.jpg){#fig-tinyml-example}
 
-:::{#exr-tinyml.callout-caution collapse="true"}
+:::{#exr-tinyml .callout-caution collapse="true"}
 
 ### TinyML with Arduino
 
@@ -278,7 +278,7 @@ These slides are a valuable tool for instructors to deliver lectures and for stu
 
 :::
 
-:::{.callout-caution collapse="false"}
+:::{ .callout-caution collapse="false"}
 #### Exercises 
 
 To reinforce the concepts covered in this chapter, we have curated a set of exercises that challenge students to apply their knowledge and deepen their understanding. 

contents/ondevice_learning/ondevice_learning.qmd

@@ -296,7 +296,7 @@ There are advantages to reusing the features:
 1. **Hierarchical Feature Learning:** Deep learning models, particularly Convolutional Neural Networks (CNNs), can learn hierarchical features. Lower layers typically learn generic features like edges and shapes, while higher layers learn more complex and task-specific features. Transfer learning allows us to reuse the generic features learned by a model and finetune the higher layers for our specific task.
 2. **Boosting Performance:** Transfer learning has been proven to boost the performance of models on tasks with limited data. The knowledge gained from the source task can provide a valuable starting point and lead to faster convergence and improved accuracy on the target task.
 
-:::{#exr-tlb.callout-caution collapse="true"}
+:::{#exr-tlb .callout-caution collapse="true"}
 
 ### Transfer Learning
 
@@ -478,7 +478,7 @@ To accomplish this goal, Google employed its algorithm DP-FTRL, which provides a
 
 ![Differential Privacy in G Board. Credit: Zheng et al., ([2023](https://arxiv.org/abs/2305.18465)).](images/png/ondevice_gboard_approach.png){#fig-differential-privacy}
 
-:::{#exr-flg.callout-caution collapse="true"}
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 ### Federated Learning - Text Generation
 
@@ -488,7 +488,7 @@ Have you ever used those smart keyboards to suggest the next word? With federate
 
 :::
 
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 ### Federated Learning - Image Classification
 
@@ -734,7 +734,7 @@ These slides serve as a valuable tool for instructors to deliver lectures and fo
 
 :::
 
-:::{.callout-caution collapse="false"}
+:::{ .callout-caution collapse="false"}
 #### Exercises 
 
 

contents/ops/ops.qmd

@@ -728,7 +728,7 @@ Deploying low-powered devices also presents inherent challenges. Optimized model
 
 While impressive results are achievable, users shouldn't view Edge Impulse as a "Push Button ML" solution. Careful project scoping, data collection, model evaluation, and testing are still essential. As with any development tool, reasonable expectations and diligence in application are advised. However, Edge Impulse can accelerate embedded ML prototyping and deployment for developers willing to invest the requisite data science and engineering effort.
 
-:::{#exr-ei.callout-caution collapse="true"}
+:::{#exr-ei .callout-caution collapse="true"}
 
 ### Edge Impulse
 
@@ -921,7 +921,7 @@ These slides serve as a valuable tool for instructors to deliver lectures and fo
 
 :::
 
-:::{.callout-caution collapse="false"}
+:::{ .callout-caution collapse="false"}
 #### Exercises 
 
 To reinforce the concepts covered in this chapter, we have curated a set of exercises that challenge students to apply their knowledge and deepen their understanding. 

contents/optimizations/optimizations.qmd

@@ -227,7 +227,7 @@ Last but not least, adherence to legal and ethical guidelines is paramount, espe
 
 ![Sparse weight matrix.](images/jpg/modeloptimization_sprase_matrix.jpeg){#fig-sparse-matrix}
 
-:::{#exr-p.callout-caution collapse="true"}
+:::{#exr-p .callout-caution collapse="true"}
 
 ### Pruning
 
@@ -299,7 +299,7 @@ The work of Tamara G. Kolda and Brett W. Bader, ["Tensor Decompositions and Appl
 
 ![Tensor decomposition. Credit: @xinyu.](images/png/modeloptimization_tensor_decomposition.png){#fig-tensor-decomposition}
 
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+:::{#exr-mc .callout-caution collapse="true"}
 
 ### Scalable Model Compression with TensorFlow
 
@@ -343,7 +343,7 @@ Similarly, MorphNet is a neural network optimization framework designed to autom
 
 TinyNAS and MorphNet represent a few of the many significant advancements in the field of systematic neural network optimization, allowing architectures to be systematically chosen and generated to fit perfectly within problem constraints.
 
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 ### Edge-Aware Model Design
 
@@ -977,7 +977,7 @@ These slides serve as a valuable tool for instructors to deliver lectures and fo
 
 :::
 
-:::{.callout-caution collapse="false"}
+:::{ .callout-caution collapse="false"}
 #### Exercises 
 
 To reinforce the concepts covered in this chapter, we have curated a set of exercises that challenge students to apply their knowledge and deepen their understanding. 

contents/privacy_security/privacy_security.qmd

@@ -831,7 +831,7 @@ The system uses hardware-based secure random number generation to sample from th
 
 Multiple third-party audits have verified that Apple's system provides rigorous differential privacy protections in line with their stated policies. Of course, assumptions around composition over time and potential re-identification risks still apply. Apple's deployment shows how differential Privacy can be realized in large real-world products when backed by sufficient engineering resources.
 
-:::{#exr-dptf.callout-caution collapse="true"}
+:::{#exr-dptf .callout-caution collapse="true"}
 
 ### Differential Privacy - TensorFlow Privacy 
 
@@ -965,7 +965,7 @@ For many real-time and embedded applications, fully homomorphic encryption remai
 
 **Hybrid Designs:** Rather than encrypting entire workflows, selective application of homomorphic encryption to critical subcomponents can achieve protection while minimizing overheads.
 
-:::{#exr-he.callout-caution collapse="true"}
+:::{#exr-he .callout-caution collapse="true"}
 
 ### Homomorphic Encryption
 
@@ -1110,7 +1110,7 @@ These slides are a valuable tool for instructors to deliver lectures and for stu
 
 :::
 
-:::{.callout-caution collapse="false"}
+:::{ .callout-caution collapse="false"}
 #### Exercises 
 
 To reinforce the concepts covered in this chapter, we have curated a set of exercises that challenge students to apply their knowledge and deepen their understanding. 

contents/responsible_ai/responsible_ai.qmd

@@ -472,7 +472,7 @@ These slides are a valuable tool for instructors to deliver lectures and for stu
 
 :::
 
-:::{.callout-caution collapse="false"}
+:::{ .callout-caution collapse="false"}
 #### Exercises 
 
 To reinforce the concepts covered in this chapter, we have curated a set of exercises that challenge students to apply their knowledge and deepen their understanding.