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ai_for_good.html

@@ -486,16 +486,14 @@ <h1 class="title"><span class="chapter-number">18</span>&nbsp; <span class="chap
 <p>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.</p>
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 <li><p>Understand how TinyML can help advance the UN Sustainable Development Goals in areas like health, agriculture, education, and the environment.</p></li>
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 <p>To give ourselves a framework around which to think about AI for social good, we will be following the UN Sustainable Development Goals (SDGs). The UN SDGs are a collection of 17 global goals adopted by the United Nations in 2015 as part of the 2030 Agenda for Sustainable Development. The SDGs address global challenges related to poverty, inequality, climate change, environmental degradation, prosperity, and peace and justice.</p>

benchmarking.html

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 <p>Benchmarking is a critical part of developing and deploying machine learning systems, especially for tinyML applications. Benchmarks allow developers to measure and compare the performance of different model architectures, training procedures, and deployment strategies. This provides key insights into which approaches work best for the problem at hand and the constraints of the deployment environment.</p>
 <p>This chapter will provide an overview of popular ML benchmarks, best practices for benchmarking, and how to use benchmarks to improve model development and system performance. It aims to provide developers with the right tools and knowledge to effectively benchmark and optimize their systems, especially for tinyML systems.</p>
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 <ul>
 <li><p>Understand the purpose and goals of benchmarking AI systems, including performance assessment, resource evaluation, validation, and more.</p></li>
 <li><p>Learn about the different types of benchmarks - micro, macro, and end-to-end - and their role in evaluating different aspects of an AI system.</p></li>
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 <p>Benchmarking provides the essential measurements needed to drive progress in machine learning and to truly understand system performance. As the physicist Lord Kelvin famously said, “To measure is to know.” Benchmarks give us the ability to know the capabilities of different models, software, and hardware quantitatively. They allow ML developers to measure the inference time, memory usage, power consumption, and other metrics that characterize a system. Moreover, benchmarks create standardized processes for measurement, enabling fair comparisons across different solutions.</p>

case_studies.html

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contributors.html

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-<a href="https://github.com/Naeemkh"><img src="https://avatars.githubusercontent.com/Naeemkh?s=100" width="100px;" alt="naeemkh"><br><sub><b>naeemkh</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=Naeemkh" title="Documentation">📖</a>
+<a href="https://github.com/DivyaAmirtharaj"><img src="https://avatars.githubusercontent.com/DivyaAmirtharaj?s=100" width="100px;" alt="Divya"><br><sub><b>Divya</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=DivyaAmirtharaj" title="Documentation">📖</a>
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-<a href="https://github.com/Mjrovai"><img src="https://avatars.githubusercontent.com/Mjrovai?s=100" width="100px;" alt="Marcelo Rovai"><br><sub><b>Marcelo Rovai</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=Mjrovai" title="Documentation">📖</a>
+<a href="https://github.com/marcozennaro"><img src="https://avatars.githubusercontent.com/marcozennaro?s=100" width="100px;" alt="Marco Zennaro"><br><sub><b>Marco Zennaro</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=marcozennaro" title="Documentation">📖</a>
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-<a href="https://github.com/sophiacho1"><img src="https://avatars.githubusercontent.com/sophiacho1?s=100" width="100px;" alt="sophiacho1"><br><sub><b>sophiacho1</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=sophiacho1" title="Documentation">📖</a>
+<a href="https://github.com/sjohri20"><img src="https://avatars.githubusercontent.com/sjohri20?s=100" width="100px;" alt="sjohri20"><br><sub><b>sjohri20</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=sjohri20" title="Documentation">📖</a>
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-<a href="https://github.com/ShvetankPrakash"><img src="https://avatars.githubusercontent.com/ShvetankPrakash?s=100" width="100px;" alt="Shvetank Prakash"><br><sub><b>Shvetank Prakash</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=ShvetankPrakash" title="Documentation">📖</a>
+<a href="https://github.com/Mjrovai"><img src="https://avatars.githubusercontent.com/Mjrovai?s=100" width="100px;" alt="Marcelo Rovai"><br><sub><b>Marcelo Rovai</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=Mjrovai" title="Documentation">📖</a>
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-<a href="https://github.com/sjohri20"><img src="https://avatars.githubusercontent.com/sjohri20?s=100" width="100px;" alt="sjohri20"><br><sub><b>sjohri20</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=sjohri20" title="Documentation">📖</a>
+<a href="https://github.com/sophiacho1"><img src="https://avatars.githubusercontent.com/sophiacho1?s=100" width="100px;" alt="sophiacho1"><br><sub><b>sophiacho1</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=sophiacho1" title="Documentation">📖</a>
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-<a href="https://github.com/mmaz"><img src="https://avatars.githubusercontent.com/mmaz?s=100" width="100px;" alt="Mark Mazumder"><br><sub><b>Mark Mazumder</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=mmaz" title="Documentation">📖</a>
+<a href="https://github.com/uchendui"><img src="https://avatars.githubusercontent.com/uchendui?s=100" width="100px;" alt="Ikechukwu Uchendu"><br><sub><b>Ikechukwu Uchendu</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=uchendui" title="Documentation">📖</a>
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-<a href="https://github.com/DivyaAmirtharaj"><img src="https://avatars.githubusercontent.com/DivyaAmirtharaj?s=100" width="100px;" alt="Divya"><br><sub><b>Divya</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=DivyaAmirtharaj" title="Documentation">📖</a>
+<a href="https://github.com/mpstewart1"><img src="https://avatars.githubusercontent.com/mpstewart1?s=100" width="100px;" alt="Matthew Stewart"><br><sub><b>Matthew Stewart</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=mpstewart1" title="Documentation">📖</a>
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-<a href="https://github.com/jessicaquaye"><img src="https://avatars.githubusercontent.com/jessicaquaye?s=100" width="100px;" alt="Jessica Quaye"><br><sub><b>Jessica Quaye</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=jessicaquaye" title="Documentation">📖</a>
+<a href="https://github.com/colbybanbury"><img src="https://avatars.githubusercontent.com/colbybanbury?s=100" width="100px;" alt="Colby Banbury"><br><sub><b>Colby Banbury</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=colbybanbury" title="Documentation">📖</a>
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-<a href="https://github.com/oishib"><img src="https://avatars.githubusercontent.com/oishib?s=100" width="100px;" alt="oishib"><br><sub><b>oishib</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=oishib" title="Documentation">📖</a>
+<a href="https://github.com/mmaz"><img src="https://avatars.githubusercontent.com/mmaz?s=100" width="100px;" alt="Mark Mazumder"><br><sub><b>Mark Mazumder</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=mmaz" title="Documentation">📖</a>
 </td>
 <td align="center" valign="top" width="14.28%">
-<a href="https://github.com/profvjreddi"><img src="https://avatars.githubusercontent.com/profvjreddi?s=100" width="100px;" alt="Vijay Janapa Reddi"><br><sub><b>Vijay Janapa Reddi</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=profvjreddi" title="Documentation">📖</a>
+<a href="https://github.com/Naeemkh"><img src="https://avatars.githubusercontent.com/Naeemkh?s=100" width="100px;" alt="naeemkh"><br><sub><b>naeemkh</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=Naeemkh" title="Documentation">📖</a>
 </td>
 <td align="center" valign="top" width="14.28%">
-<a href="https://github.com/BaeHenryS"><img src="https://avatars.githubusercontent.com/BaeHenryS?s=100" width="100px;" alt="Henry Bae"><br><sub><b>Henry Bae</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=BaeHenryS" title="Documentation">📖</a>
+<a href="https://github.com/ShvetankPrakash"><img src="https://avatars.githubusercontent.com/ShvetankPrakash?s=100" width="100px;" alt="Shvetank Prakash"><br><sub><b>Shvetank Prakash</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=ShvetankPrakash" title="Documentation">📖</a>
 </td>
 <td align="center" valign="top" width="14.28%">
-<a href="https://github.com/mpstewart1"><img src="https://avatars.githubusercontent.com/mpstewart1?s=100" width="100px;" alt="Matthew Stewart"><br><sub><b>Matthew Stewart</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=mpstewart1" title="Documentation">📖</a>
+<a href="https://github.com/oishib"><img src="https://avatars.githubusercontent.com/oishib?s=100" width="100px;" alt="oishib"><br><sub><b>oishib</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=oishib" title="Documentation">📖</a>
 </td>
 <td align="center" valign="top" width="14.28%">
-<a href="https://github.com/marcozennaro"><img src="https://avatars.githubusercontent.com/marcozennaro?s=100" width="100px;" alt="Marco Zennaro"><br><sub><b>Marco Zennaro</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=marcozennaro" title="Documentation">📖</a>
+<a href="https://github.com/BaeHenryS"><img src="https://avatars.githubusercontent.com/BaeHenryS?s=100" width="100px;" alt="Henry Bae"><br><sub><b>Henry Bae</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=BaeHenryS" title="Documentation">📖</a>
 </td>
 <td align="center" valign="top" width="14.28%">
-<a href="https://github.com/colbybanbury"><img src="https://avatars.githubusercontent.com/colbybanbury?s=100" width="100px;" alt="Colby Banbury"><br><sub><b>Colby Banbury</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=colbybanbury" title="Documentation">📖</a>
+<a href="https://github.com/profvjreddi"><img src="https://avatars.githubusercontent.com/profvjreddi?s=100" width="100px;" alt="Vijay Janapa Reddi"><br><sub><b>Vijay Janapa Reddi</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=profvjreddi" title="Documentation">📖</a>
 </td>
 </tr>
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-<a href="https://github.com/uchendui"><img src="https://avatars.githubusercontent.com/uchendui?s=100" width="100px;" alt="Ikechukwu Uchendu"><br><sub><b>Ikechukwu Uchendu</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=uchendui" title="Documentation">📖</a>
+<a href="https://github.com/ishapira1"><img src="https://avatars.githubusercontent.com/ishapira1?s=100" width="100px;" alt="ishapira"><br><sub><b>ishapira</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=ishapira1" title="Documentation">📖</a>
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-<a href="https://github.com/ishapira1"><img src="https://avatars.githubusercontent.com/ishapira1?s=100" width="100px;" alt="ishapira"><br><sub><b>ishapira</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=ishapira1" title="Documentation">📖</a>
+<a href="https://github.com/jessicaquaye"><img src="https://avatars.githubusercontent.com/jessicaquaye?s=100" width="100px;" alt="Jessica Quaye"><br><sub><b>Jessica Quaye</b></sub></a><br><a href="https=//github.com/harvard-edge/cs249r_book/commits?author=jessicaquaye" title="Documentation">📖</a>
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data_engineering.html

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 <p>Data is the lifeblood of AI systems. Without good data, even the most advanced machine learning algorithms will fail. In this section, we will dive into the intricacies of building high-quality datasets to fuel our AI models. Data engineering encompasses the processes of collecting, storing, processing, and managing data for training machine learning models.</p>
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 <p>Dataset creators face complex privacy and representation challenges when building high-quality training data, especially for sensitive domains like healthcare. Legally, creators may need to remove direct identifiers like names and ages. Even without legal obligations, removing such information can help build user trust. However, excessive anonymization can compromise dataset utility. Techniques like differential privacy<span class="math inline">\(^{1}\)</span>, aggregation, and reducing detail provide alternatives to balance privacy and utility, but have downsides. Creators must strike a thoughtful balance based on use case.</p>

dl_primer.html

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 <p>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.</p>
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