From 5cdaf66c009ddd538ee046a76d339a0b492b5fb0 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Wed, 4 Dec 2024 11:59:05 -0500 Subject: [PATCH] Added in mobile ML section --- contents/core/ml_systems/ml_systems.qmd | 179 +++++++++++++++++------- 1 file changed, 132 insertions(+), 47 deletions(-) diff --git a/contents/core/ml_systems/ml_systems.qmd b/contents/core/ml_systems/ml_systems.qmd index 5477580e..d96c9fbc 100644 --- a/contents/core/ml_systems/ml_systems.qmd +++ b/contents/core/ml_systems/ml_systems.qmd @@ -14,18 +14,19 @@ Machine learning (ML) systems, built on the foundation of computing systems, hol As this chapter progresses, we will explore ML systems' complex and fascinating world. We'll gain insights into their structural design and operational features and understand their key role in powering ML applications. Starting with the basics of microcontroller units, we will examine the interfaces and peripherals that improve their functionalities. This chapter is designed to be a comprehensive guide that explains the nuanced aspects of different ML systems. -::: callout-tip +:::{.callout-tip} + ## Learning Objectives -- Understand the key characteristics and differences between Cloud ML, Edge ML, Mobile ML, and TinyML systems. +- Understand the key characteristics and differences between Cloud ML, Edge ML, Mobile ML, and TinyML systems. -- Analyze the benefits and challenges associated with each ML paradigm. +- Analyze the benefits and challenges associated with each ML paradigm. -- Explore real-world applications and use cases for Cloud ML, Edge ML, Mobile ML, and TinyML. +- Explore real-world applications and use cases for Cloud ML, Edge ML, Mobile ML, and TinyML. -- Compare the performance aspects of each ML approach, including latency, privacy, and resource utilization. +- Compare the performance aspects of each ML approach, including latency, privacy, and resource utilization. -- Examine the evolving landscape of ML systems and potential future developments. +- Examine the evolving landscape of ML systems and potential future developments. ::: ## Overview @@ -50,10 +51,10 @@ The evolution of machine learning systems can be seen as a progression from cent Each of these paradigms has its own strengths and is suited to different use cases: -- Cloud ML remains essential for tasks requiring massive computational power or large-scale data analysis. -- Edge ML is ideal for applications needing low-latency responses or local data processing in industrial or enterprise environments. -- Mobile ML is suited for personalized, responsive applications on smartphones and tablets. -- TinyML enables AI capabilities in small, power-efficient devices, expanding the reach of ML to new domains. +- Cloud ML remains essential for tasks requiring massive computational power or large-scale data analysis. +- Edge ML is ideal for applications needing low-latency responses or local data processing in industrial or enterprise environments. +- Mobile ML is suited for personalized, responsive applications on smartphones and tablets. +- TinyML enables AI capabilities in small, power-efficient devices, expanding the reach of ML to new domains. This progression reflects a broader trend in computing towards more distributed, localized, and specialized processing. The evolution is driven by the need for faster response times, improved privacy, reduced bandwidth usage, and the ability to operate in environments with limited or no connectivity, while also catering to the specific capabilities and constraints of different types of devices. @@ -63,16 +64,12 @@ This progression reflects a broader trend in computing towards more distributed, ## Cloud ML -Cloud ML leverages powerful servers in the cloud for training and running large, complex ML models and relies on internet connectivity. @fig-cloud-ml provides an overview of Cloud ML's capabilities which we will discuss in greater detail throughout this section. +Cloud Machine Learning (Cloud ML) is a subfield of machine learning that leverages the power and scalability of cloud computing infrastructure to develop, train, and deploy machine learning models. By utilizing the vast computational resources available in the cloud, Cloud ML enables the efficient handling of large-scale datasets and complex machine learning algorithms. @fig-cloud-ml provides an overview of Cloud ML's capabilities which we will discuss in greater detail throughout this section. ![Section overview for Cloud ML.](images/png/cloudml.png){#fig-cloud-ml} ### Characteristics -#### Definition of Cloud ML - -Cloud Machine Learning (Cloud ML) is a subfield of machine learning that leverages the power and scalability of cloud computing infrastructure to develop, train, and deploy machine learning models. By utilizing the vast computational resources available in the cloud, Cloud ML enables the efficient handling of large-scale datasets and complex machine learning algorithms. - #### Centralized Infrastructure One of the key characteristics of Cloud ML is its centralized infrastructure. @fig-cloudml-example illustrates this concept with an example from Google's Cloud TPU data center. Cloud service providers offer a virtual platform that consists of high-capacity servers, expansive storage solutions, and robust networking architectures, all housed in data centers distributed across the globe. As shown in the figure, these centralized facilities can be massive in scale, housing rows upon rows of specialized hardware. This centralized setup allows for the pooling and efficient management of computational resources, making it easier to scale machine learning projects as needed. @@ -175,14 +172,12 @@ Cloud ML plays a role in bolstering user security by powering anomaly detection ## Edge ML -### Characteristics - -#### Definition of Edge ML - Edge Machine Learning (Edge ML) runs machine learning algorithms directly on endpoint devices or closer to where the data is generated rather than relying on centralized cloud servers. This approach brings computation closer to the data source, reducing the need to send large volumes of data over networks, often resulting in lower latency and improved data privacy. @fig-edge-ml provides an overview of this section. ![Section overview for Edge ML.](images/png/edgeml.png){#fig-edge-ml} +### Characteristics + #### Decentralized Data Processing In Edge ML, data processing happens in a decentralized fashion, as illustrated in @fig-edgeml-example. Instead of sending data to remote servers, the data is processed locally on devices like smartphones, tablets, or Internet of Things (IoT) devices. The figure showcases various examples of these edge devices, including wearables, industrial sensors, and smart home appliances. This local processing allows devices to make quick decisions based on the data they collect without relying heavily on a central server's resources. @@ -239,19 +234,79 @@ The Industrial IoT leverages Edge ML to monitor and control complex industrial p The applicability of Edge ML is vast and not limited to these examples. Various other sectors, including healthcare, agriculture, and urban planning, are exploring and integrating Edge ML to develop innovative solutions responsive to real-world needs and challenges, heralding a new era of smart, interconnected systems. -## Tiny ML +## Mobile ML + +Mobile Machine Learning (Mobile ML) represents a specialized branch of Edge ML that focuses on deploying and running machine learning models directly on mobile devices like smartphones and tablets. This approach leverages the computational capabilities of modern mobile processors to perform ML tasks locally, offering a balance between the power of edge computing and the ubiquity of personal devices. ### Characteristics -#### Definition of TinyML +#### On-Device Processing + +Mobile ML utilizes the processing power of mobile devices' System-on-Chip (SoC) architectures, including specialized Neural Processing Units (NPUs) and AI accelerators. This enables efficient execution of ML models directly on the device, allowing for real-time processing of data from device sensors like cameras, microphones, and motion sensors without constant cloud connectivity. + +#### Optimized Frameworks + +Mobile ML is supported by specialized frameworks and tools designed specifically for mobile deployment, such as TensorFlow Lite for Android devices and Core ML for iOS devices. These frameworks are optimized for mobile hardware and provide efficient model compression and quantization techniques to ensure smooth performance within mobile resource constraints. + +### Benefits + +#### Real-Time Processing + +Mobile ML enables real-time processing of data directly on mobile devices, eliminating the need for constant server communication. This results in faster response times for applications requiring immediate feedback, such as real-time translation, face detection, or gesture recognition. + +#### Privacy Preservation + +By processing data locally on the device, Mobile ML helps maintain user privacy. Sensitive information doesn't need to leave the device, reducing the risk of data breaches and addressing privacy concerns, particularly important for applications handling personal data. + +#### Offline Functionality + +Mobile ML applications can function without constant internet connectivity, making them reliable in areas with poor network coverage or when users are offline. This ensures consistent performance and user experience regardless of network conditions. + +### Challenges + +#### Resource Constraints + +Despite modern mobile devices being powerful, they still face resource constraints compared to cloud servers. Mobile ML must operate within limited RAM, storage, and processing power, requiring careful optimization of models and efficient resource management. + +#### Battery Life Impact + +ML operations can be computationally intensive, potentially impacting device battery life. Developers must balance model complexity and performance with power consumption to ensure reasonable battery life for users. + +#### Model Size Limitations + +Mobile devices have limited storage space, necessitating careful consideration of model size. This often requires model compression and quantization techniques, which can affect model accuracy and performance. + +### Example Use Cases + +#### Computer Vision Applications + +Mobile ML has revolutionized how we use cameras on mobile devices, enabling sophisticated computer vision applications that process visual data in real-time. Modern smartphone cameras now incorporate ML models that can detect faces, analyze scenes, and apply complex filters instantaneously. These models work directly on the camera feed to enable features like portrait mode photography, where ML algorithms separate foreground subjects from backgrounds. Document scanning applications use ML to detect paper edges, correct perspective, and enhance text readability, while augmented reality applications use ML-powered object detection to accurately place virtual objects in the real world. + +#### Natural Language Processing + +Natural language processing on mobile devices has transformed how we interact with our phones and communicate with others. Speech recognition models run directly on device, enabling voice assistants to respond quickly to commands even without internet connectivity. Real-time translation applications can now translate conversations and text without sending data to the cloud, preserving privacy and working reliably regardless of network conditions. Mobile keyboards have become increasingly intelligent, using ML to predict not just the next word but entire phrases based on the user's writing style and context, while maintaining all learning and personalization locally on the device. + +#### Health and Fitness Monitoring + +Mobile ML has enabled smartphones and tablets to become sophisticated health monitoring devices. Through clever use of existing sensors combined with ML models, mobile devices can now track physical activity, analyze sleep patterns, and monitor vital signs. For example, cameras can measure heart rate by detecting subtle color changes in the user's skin, while accelerometers and ML models work together to recognize specific exercises and analyze workout form. These applications process sensitive health data directly on the device, ensuring privacy while providing users with real-time feedback and personalized health insights. + +#### Personalization and User Experience + +Perhaps the most pervasive but least visible application of Mobile ML lies in how it personalizes and enhances the overall user experience. ML models continuously analyze how users interact with their devices to optimize everything from battery usage to interface layouts. These models learn individual usage patterns to predict which apps users are likely to open next, preload content they might want to see, and adjust system settings like screen brightness and audio levels based on environmental conditions and user preferences. This creates a deeply personalized experience that adapts to each user's needs while maintaining privacy by keeping all learning and adaptation on the device itself. + +These applications demonstrate how Mobile ML bridges the gap between cloud-based solutions and edge computing, providing efficient, privacy-conscious, and user-friendly machine learning capabilities on personal mobile devices. The continuous advancement in mobile hardware capabilities and optimization techniques continues to expand the possibilities for Mobile ML applications. + +## Tiny ML TinyML sits at the crossroads of embedded systems and machine learning, representing a burgeoning field that brings smart algorithms directly to tiny microcontrollers and sensors. These microcontrollers operate under severe resource constraints, particularly regarding memory, storage, and computational power. @fig-tiny-ml encapsulates the key aspects of TinyML discussed in this section. ![Section overview for Tiny ML.](images/png/tinyml.png){#fig-tiny-ml} +### Characteristics + #### On-Device Machine Learning -In TinyML, the focus is on on-device machine learning. This means that machine learning models are deployed and trained on the device, eliminating the need for external servers or cloud infrastructures. This allows TinyML to enable intelligent decision-making right where the data is generated, making real-time insights and actions possible, even in settings where connectivity is limited or unavailable. +In TinyML, the focus, much like in Mobile ML, is on on-device machine learning. This means that machine learning models are deployed and trained on the device, eliminating the need for external servers or cloud infrastructures. This allows TinyML to enable intelligent decision-making right where the data is generated, making real-time insights and actions possible, even in settings where connectivity is limited or unavailable. #### Low Power and Resource-Constrained Environments @@ -260,6 +315,7 @@ TinyML excels in low-power and resource-constrained settings. These environments ![Examples of TinyML device kits. Source: [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"} + ### TinyML with Arduino Get ready to bring machine learning to the smallest of devices! In the embedded machine learning world, TinyML is where resource constraints meet ingenuity. This Colab notebook will walk you through building a gesture recognition model designed on an Arduino board. You'll learn how to train a small but effective neural network, optimize it for minimal memory usage, and deploy it to your microcontroller. If you're excited about making everyday objects smarter, this is where it begins! @@ -323,19 +379,45 @@ Let's bring together the different ML variants we've explored individually for a For a more detailed comparison of these ML variants, we can refer to @tbl-big_vs_tiny. This table offers a comprehensive analysis of Cloud ML, Edge ML, and TinyML based on various features and aspects. By examining these different characteristics side by side, we gain a clearer perspective on the unique advantages and distinguishing factors of each approach. This detailed comparison, combined with the visual overview provided by the Venn diagram, aids in making informed decisions based on the specific needs and constraints of a given application or project. -| Aspect | Cloud ML | Edge ML | TinyML | -|:---------------|:------------------|:------------------|:------------------| -| Processing Location | Centralized servers (Data Centers) | Local devices (closer to data sources) | On-device (microcontrollers, embedded systems) | -| Latency | High (Depends on internet connectivity) | Moderate (Reduced latency compared to Cloud ML) | Low (Immediate processing without network delay) | -| Data Privacy | Moderate (Data transmitted over networks) | High (Data remains on local networks) | Very High (Data processed on-device, not transmitted) | -| Computational Power | High (Utilizes powerful data center infrastructure) | Moderate (Utilizes local device capabilities) | Low (Limited to the power of the embedded system) | -| Energy Consumption | High (Data centers consume significant energy) | Moderate (Less than data centers, more than TinyML) | Low (Highly energy-efficient, designed for low power) | -| Scalability | High (Easy to scale with additional server resources) | Moderate (Depends on local device capabilities) | Low (Limited by the hardware resources of the device) | -| Cost | High (Recurring costs for server usage, maintenance) | Variable (Depends on the complexity of local setup) | Low (Primarily upfront costs for hardware components) | -| Connectivity | High (Requires stable internet connectivity) | Low (Can operate with intermittent connectivity) | Very Low (Can operate without any network connectivity) | -| Real-time Processing | Moderate (Can be affected by network latency) | High (Capable of real-time processing locally) | Very High (Immediate processing with minimal latency) | -| Application Examples | Big Data Analysis, Virtual Assistants | Autonomous Vehicles, Smart Homes | Wearables, Sensor Networks | -| Complexity | Moderate to High (Requires knowledge in cloud computing) | Moderate (Requires knowledge in local network setup) | Moderate to High (Requires expertise in embedded systems) | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Aspect | Cloud ML | Edge ML | Mobile ML | TinyML | ++:=========================+:=========================================================+:=========================================================+:==========================================================+:=========================================================+ +| Processing Location | Centralized cloud servers (Data Centers) | Local edge devices (gateways, servers) | Smartphones and tablets | Ultra-low-power microcontrollers and embedded systems | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Latency | High (100ms-1000ms+) | Moderate (10-100ms) | Low-Moderate (5-50ms) | Very Low (1-10ms) | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Data Privacy | Basic-Moderate (Data leaves device) | High (Data stays in local network) | High (Data stays on phone) | Very High (Data never leaves sensor) | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Computational Power | Very High (Multiple GPUs/TPUs) | High (Edge GPUs) | Moderate (Mobile NPUs/GPUs) | Very Low (MCU/tiny processors) | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Energy Consumption | Very High (kW-MW range) | High (100s W) | Moderate (1-10W) | Very Low (mW range) | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Scalability | Excellent (virtually unlimited) | Good (limited by edge hardware) | Moderate (per-device scaling) | Limited (fixed hardware) | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Cost | High ($1000s+/month) | Moderate ($100s-1000s) | Low ($0-10s) | Very Low ($1-10s) | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Connectivity Required | Constant high-bandwidth | Intermittent | Optional | None | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Real-time Processing | Dependent on network | Good | Very Good | Excellent | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Storage Capacity | Unlimited (petabytes+) | Large (terabytes) | Moderate (gigabytes) | Very Limited (kilobytes-megabytes) | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Primary Use Cases | Big Data Analytics, Training, Complex AI Models | Smart Manufacturing, Video Analytics, IoT Hubs | AR/VR Apps, Mobile Gaming, Photo/Video Processing | Sensor Processing, Gesture Detection, Keyword Spotting | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Development Complexity | High (cloud expertise needed) | Moderate-High (edge+networking) | Moderate (mobile SDKs) | High (embedded expertise) | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Deployment Speed | Fast | Moderate | Fast | Slow | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Hardware Requirements | Cloud infrastructure | Edge servers/gateways | Modern smartphones | MCUs/embedded systems | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Framework Support | All ML frameworks | Most frameworks | Mobile-optimized (TFLite, CoreML) | TinyML frameworks | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Model Size Limits | None | Several GB | 10s-100s MB | Bytes-KB range | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Battery Impact | N/A | N/A | Moderate | Minimal | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ +| Offline Capability | None | Good | Excellent | Complete | ++--------------------------+----------------------------------------------------------+----------------------------------------------------------+-----------------------------------------------------------+----------------------------------------------------------+ : Comparison of feature aspects across Cloud ML, Edge ML, and TinyML. {#tbl-big_vs_tiny .hover .striped} @@ -352,41 +434,44 @@ The embedded ML landscape is rapidly evolving and poised to enable intelligent a Here is a curated list of resources to support students and instructors in their learning and teaching journeys. We are continuously working on expanding this collection and will be adding new exercises soon. ::: {.callout-note collapse="false"} + #### Slides These slides are a valuable tool for instructors to deliver lectures and for students to review the material at their own pace. We encourage students and instructors to leverage these slides to improve their understanding and facilitate effective knowledge transfer. -- [Embedded Systems Overview.](https://docs.google.com/presentation/d/1Lgrn7bddHYxyrOmk0JfSVmEBimRePqI7WSliUKRPK9E/edit?resourcekey=0-c5JvfDeqHIdV9A5RMAMAyw#slide=id.g94db9f9f78_0_8) +- [Embedded Systems Overview.](https://docs.google.com/presentation/d/1Lgrn7bddHYxyrOmk0JfSVmEBimRePqI7WSliUKRPK9E/edit?resourcekey=0-c5JvfDeqHIdV9A5RMAMAyw#slide=id.g94db9f9f78_0_8) -- [Embedded Computer Hardware.](https://docs.google.com/presentation/d/1hDCFcOrZ08kZPhY4DA3gVikGUo47HwNyvqNrLW-t-Tg/edit?resourcekey=0-J6ix5AYvZMGbFFOa7ae4Hw#slide=id.g94db9f9f78_0_8) +- [Embedded Computer Hardware.](https://docs.google.com/presentation/d/1hDCFcOrZ08kZPhY4DA3gVikGUo47HwNyvqNrLW-t-Tg/edit?resourcekey=0-J6ix5AYvZMGbFFOa7ae4Hw#slide=id.g94db9f9f78_0_8) -- [Embedded I/O.](https://docs.google.com/presentation/d/1rnWh9XC6iCKSx_hQd4xq2iIDlpc-GkBQw_GjzlP5mQc/edit#slide=id.g94db9f9f78_0_8) +- [Embedded I/O.](https://docs.google.com/presentation/d/1rnWh9XC6iCKSx_hQd4xq2iIDlpc-GkBQw_GjzlP5mQc/edit#slide=id.g94db9f9f78_0_8) -- [Embedded systems software.](https://docs.google.com/presentation/d/1TApZn9xxPWCRY-D-soJ8YOSsfysnccR5UjOyspzeTuU/edit?resourcekey=0-BRWIyCKPLNQFnIfG0fJJ9A#slide=id.g94db9f9f78_0_8) +- [Embedded systems software.](https://docs.google.com/presentation/d/1TApZn9xxPWCRY-D-soJ8YOSsfysnccR5UjOyspzeTuU/edit?resourcekey=0-BRWIyCKPLNQFnIfG0fJJ9A#slide=id.g94db9f9f78_0_8) -- [Embedded ML software.](https://docs.google.com/presentation/d/17wgAfoF24Rcx7uPrbau0c8FyzXIUWbe48qGGBOXXT-g/edit?resourcekey=0-Uv29DvmF7gYzKdOoRtn0vw#slide=id.g94db9f9f78_0_8) +- [Embedded ML software.](https://docs.google.com/presentation/d/17wgAfoF24Rcx7uPrbau0c8FyzXIUWbe48qGGBOXXT-g/edit?resourcekey=0-Uv29DvmF7gYzKdOoRtn0vw#slide=id.g94db9f9f78_0_8) -- [Embedded Inference.](https://docs.google.com/presentation/d/1FOUQ9dbe3l_qTa2AnroSbOz0ykuCz5cbTNO77tvFxEs/edit?usp=drive_link) +- [Embedded Inference.](https://docs.google.com/presentation/d/1FOUQ9dbe3l_qTa2AnroSbOz0ykuCz5cbTNO77tvFxEs/edit?usp=drive_link) -- [TinyML on Microcontrollers.](https://docs.google.com/presentation/d/1jwAZz3UOoJTR8PY6Wa34FxijpoDc9gBM/edit?usp=drive_link&ouid=102419556060649178683&rtpof=true&sd=true) +- [TinyML on Microcontrollers.](https://docs.google.com/presentation/d/1jwAZz3UOoJTR8PY6Wa34FxijpoDc9gBM/edit?usp=drive_link&ouid=102419556060649178683&rtpof=true&sd=true) -- TinyML as a Service (TinyMLaaS): +- TinyML as a Service (TinyMLaaS): - - [TinyMLaaS: Introduction.](https://docs.google.com/presentation/d/1O7bxb36SnexfDI3iE_p0C8JI_VYXAL8cyAx3JKDfeUo/edit?usp=drive_link) + - [TinyMLaaS: Introduction.](https://docs.google.com/presentation/d/1O7bxb36SnexfDI3iE_p0C8JI_VYXAL8cyAx3JKDfeUo/edit?usp=drive_link) - - [TinyMLaaS: Design Overview.](https://docs.google.com/presentation/d/1ZUUHtTbKlzeTwVteQMSztscQmdmMxT1A24pBKSys7g0/edit#slide=id.g94db9f9f78_0_2) + - [TinyMLaaS: Design Overview.](https://docs.google.com/presentation/d/1ZUUHtTbKlzeTwVteQMSztscQmdmMxT1A24pBKSys7g0/edit#slide=id.g94db9f9f78_0_2) ::: ::: {.callout-important collapse="false"} + #### Videos -- *Coming soon.* +- *Coming soon.* ::: ::: {.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. -- *Coming soon.* +- *Coming soon.* :::