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diff --git a/.nojekyll b/.nojekyll
@@ -1 +1 @@
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diff --git a/about.html b/about.html
@@ -8,7 +8,7 @@
 
 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
-<title>Machine Learning Systems for TinyML - About the Book</title>
+<title>MACHINE LEARNING SYSTEMS for TinyML - About the Book</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -92,20 +92,20 @@
   <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
-      <a href="./">Machine Learning Systems for TinyML</a> 
+      <a href="./">MACHINE LEARNING SYSTEMS for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
     <a href="https://github.com/harvard-edge/cs249r_book" rel="" title="Source Code" class="quarto-navigation-tool px-1" aria-label="Source Code"><i class="bi bi-github"></i></a>
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       <ul class="dropdown-menu" aria-labelledby="quarto-navigation-tool-dropdown-0">
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
+            <a class="dropdown-item sidebar-tools-main-item" href="./MACHINE-LEARNING-SYSTEMS-for-TinyML.pdf">
               <i class="bi bi-bi-file-pdf pe-1"></i>
             Download PDF
             </a>
           </li>
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./MACHINE-LEARNING-SYSTEMS-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
             </a>
@@ -393,6 +393,12 @@ <h1 class="title">About the Book</h1>
 
   </div>
 
+<div>
+  <div class="abstract">
+    <div class="abstract-title">Abstract</div>
+    Machine Learning Systems for TinyML offers comprehensive guidance on deploying machine learning on embedded devices. As edge computing and the Internet of Things proliferate, this textbook provides professionals and students the expertise to implement performant AI on resource-constrained hardware. A unique aspect of this book elucidates the entire machine learning workflow, from data engineering through training, optimization, acceleration, and production deployment. Key topics covered include deep learning and classical ML algorithms for embedded systems, efficient neural network architectures, hardware-aware training techniques, model compression, benchmarking for tinyML, and on-device learning. Additional chapters highlight cutting-edge advances like on-device data generation and crucial considerations around reliability, privacy, security, and responsible AI. With its rigorous approach spanning theory and practice across diverse tinyML application domains like smart homes, wearables, and industrial IoT, the book enables readers to develop specialized knowledge. Using concrete use cases and hands-on examples, readers will learn to apply machine learning to transform embedded and IoT systems. Overall, this indispensable guide provides a research-based foundation for leveraging machine learning in embedded systems.
+  </div>
+</div>
 
 </header>
 

diff --git a/acknowledgements.html b/acknowledgements.html
@@ -8,7 +8,7 @@
 
 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
-<title>Machine Learning Systems for TinyML - Acknowledgements</title>
+<title>MACHINE LEARNING SYSTEMS for TinyML - Acknowledgements</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -92,20 +92,20 @@
   <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
-      <a href="./">Machine Learning Systems for TinyML</a> 
+      <a href="./">MACHINE LEARNING SYSTEMS for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
     <a href="https://github.com/harvard-edge/cs249r_book" rel="" title="Source Code" class="quarto-navigation-tool px-1" aria-label="Source Code"><i class="bi bi-github"></i></a>
     <div class="dropdown">
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       <ul class="dropdown-menu" aria-labelledby="quarto-navigation-tool-dropdown-0">
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
+            <a class="dropdown-item sidebar-tools-main-item" href="./MACHINE-LEARNING-SYSTEMS-for-TinyML.pdf">
               <i class="bi bi-bi-file-pdf pe-1"></i>
             Download PDF
             </a>
           </li>
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./MACHINE-LEARNING-SYSTEMS-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
             </a>
@@ -383,6 +383,12 @@ <h1 class="title">Acknowledgements</h1>
 
   </div>
 
+<div>
+  <div class="abstract">
+    <div class="abstract-title">Abstract</div>
+    Machine Learning Systems for TinyML offers comprehensive guidance on deploying machine learning on embedded devices. As edge computing and the Internet of Things proliferate, this textbook provides professionals and students the expertise to implement performant AI on resource-constrained hardware. A unique aspect of this book elucidates the entire machine learning workflow, from data engineering through training, optimization, acceleration, and production deployment. Key topics covered include deep learning and classical ML algorithms for embedded systems, efficient neural network architectures, hardware-aware training techniques, model compression, benchmarking for tinyML, and on-device learning. Additional chapters highlight cutting-edge advances like on-device data generation and crucial considerations around reliability, privacy, security, and responsible AI. With its rigorous approach spanning theory and practice across diverse tinyML application domains like smart homes, wearables, and industrial IoT, the book enables readers to develop specialized knowledge. Using concrete use cases and hands-on examples, readers will learn to apply machine learning to transform embedded and IoT systems. Overall, this indispensable guide provides a research-based foundation for leveraging machine learning in embedded systems.
+  </div>
+</div>
 
 </header>
 

diff --git a/ai_for_good.html b/ai_for_good.html
@@ -8,7 +8,7 @@
 
 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
-<title>Machine Learning Systems for TinyML - 18&nbsp; AI for Good</title>
+<title>MACHINE LEARNING SYSTEMS for TinyML - 18&nbsp; AI for Good</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -92,20 +92,20 @@
   <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
-      <a href="./">Machine Learning Systems for TinyML</a> 
+      <a href="./">MACHINE LEARNING SYSTEMS for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
     <a href="https://github.com/harvard-edge/cs249r_book" rel="" title="Source Code" class="quarto-navigation-tool px-1" aria-label="Source Code"><i class="bi bi-github"></i></a>
     <div class="dropdown">
       <a href="" title="Download" id="quarto-navigation-tool-dropdown-0" class="quarto-navigation-tool dropdown-toggle px-1" data-bs-toggle="dropdown" aria-expanded="false" aria-label="Download"><i class="bi bi-download"></i></a>
       <ul class="dropdown-menu" aria-labelledby="quarto-navigation-tool-dropdown-0">
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
+            <a class="dropdown-item sidebar-tools-main-item" href="./MACHINE-LEARNING-SYSTEMS-for-TinyML.pdf">
               <i class="bi bi-bi-file-pdf pe-1"></i>
             Download PDF
             </a>
           </li>
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./MACHINE-LEARNING-SYSTEMS-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
             </a>
@@ -399,6 +399,12 @@ <h1 class="title"><span class="chapter-number">18</span>&nbsp; <span class="chap
 
   </div>
 
+<div>
+  <div class="abstract">
+    <div class="abstract-title">Abstract</div>
+    Machine Learning Systems for TinyML offers comprehensive guidance on deploying machine learning on embedded devices. As edge computing and the Internet of Things proliferate, this textbook provides professionals and students the expertise to implement performant AI on resource-constrained hardware. A unique aspect of this book elucidates the entire machine learning workflow, from data engineering through training, optimization, acceleration, and production deployment. Key topics covered include deep learning and classical ML algorithms for embedded systems, efficient neural network architectures, hardware-aware training techniques, model compression, benchmarking for tinyML, and on-device learning. Additional chapters highlight cutting-edge advances like on-device data generation and crucial considerations around reliability, privacy, security, and responsible AI. With its rigorous approach spanning theory and practice across diverse tinyML application domains like smart homes, wearables, and industrial IoT, the book enables readers to develop specialized knowledge. Using concrete use cases and hands-on examples, readers will learn to apply machine learning to transform embedded and IoT systems. Overall, this indispensable guide provides a research-based foundation for leveraging machine learning in embedded systems.
+  </div>
+</div>
 
 </header>
 

diff --git a/benchmarking.html b/benchmarking.html
@@ -8,7 +8,7 @@
 
 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
-<title>Machine Learning Systems for TinyML - 12&nbsp; Benchmarking AI</title>
+<title>MACHINE LEARNING SYSTEMS for TinyML - 12&nbsp; Benchmarking AI</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -92,20 +92,20 @@
   <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
-      <a href="./">Machine Learning Systems for TinyML</a> 
+      <a href="./">MACHINE LEARNING SYSTEMS for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
     <a href="https://github.com/harvard-edge/cs249r_book" rel="" title="Source Code" class="quarto-navigation-tool px-1" aria-label="Source Code"><i class="bi bi-github"></i></a>
     <div class="dropdown">
       <a href="" title="Download" id="quarto-navigation-tool-dropdown-0" class="quarto-navigation-tool dropdown-toggle px-1" data-bs-toggle="dropdown" aria-expanded="false" aria-label="Download"><i class="bi bi-download"></i></a>
       <ul class="dropdown-menu" aria-labelledby="quarto-navigation-tool-dropdown-0">
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
+            <a class="dropdown-item sidebar-tools-main-item" href="./MACHINE-LEARNING-SYSTEMS-for-TinyML.pdf">
               <i class="bi bi-bi-file-pdf pe-1"></i>
             Download PDF
             </a>
           </li>
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./MACHINE-LEARNING-SYSTEMS-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
             </a>
@@ -398,6 +398,12 @@ <h1 class="title"><span class="chapter-number">12</span>&nbsp; <span class="chap
 
   </div>
 
+<div>
+  <div class="abstract">
+    <div class="abstract-title">Abstract</div>
+    Machine Learning Systems for TinyML offers comprehensive guidance on deploying machine learning on embedded devices. As edge computing and the Internet of Things proliferate, this textbook provides professionals and students the expertise to implement performant AI on resource-constrained hardware. A unique aspect of this book elucidates the entire machine learning workflow, from data engineering through training, optimization, acceleration, and production deployment. Key topics covered include deep learning and classical ML algorithms for embedded systems, efficient neural network architectures, hardware-aware training techniques, model compression, benchmarking for tinyML, and on-device learning. Additional chapters highlight cutting-edge advances like on-device data generation and crucial considerations around reliability, privacy, security, and responsible AI. With its rigorous approach spanning theory and practice across diverse tinyML application domains like smart homes, wearables, and industrial IoT, the book enables readers to develop specialized knowledge. Using concrete use cases and hands-on examples, readers will learn to apply machine learning to transform embedded and IoT systems. Overall, this indispensable guide provides a research-based foundation for leveraging machine learning in embedded systems.
+  </div>
+</div>
 
 </header>
 

diff --git a/case_studies.html b/case_studies.html
@@ -8,7 +8,7 @@
 
 <meta name="keywords" content="open-source, embedded systems, machine learning, tinyML">
 
-<title>Machine Learning Systems for TinyML - Appendix F: Case Studies</title>
+<title>MACHINE LEARNING SYSTEMS for TinyML - Appendix F: Case Studies</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -91,20 +91,20 @@
   <nav id="quarto-sidebar" class="sidebar collapse collapse-horizontal sidebar-navigation floating overflow-auto">
     <div class="pt-lg-2 mt-2 text-left sidebar-header">
     <div class="sidebar-title mb-0 py-0">
-      <a href="./">Machine Learning Systems for TinyML</a> 
+      <a href="./">MACHINE LEARNING SYSTEMS for TinyML</a> 
         <div class="sidebar-tools-main tools-wide">
     <a href="https://github.com/harvard-edge/cs249r_book" rel="" title="Source Code" class="quarto-navigation-tool px-1" aria-label="Source Code"><i class="bi bi-github"></i></a>
     <div class="dropdown">
       <a href="" title="Download" id="quarto-navigation-tool-dropdown-0" class="quarto-navigation-tool dropdown-toggle px-1" data-bs-toggle="dropdown" aria-expanded="false" aria-label="Download"><i class="bi bi-download"></i></a>
       <ul class="dropdown-menu" aria-labelledby="quarto-navigation-tool-dropdown-0">
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.pdf">
+            <a class="dropdown-item sidebar-tools-main-item" href="./MACHINE-LEARNING-SYSTEMS-for-TinyML.pdf">
               <i class="bi bi-bi-file-pdf pe-1"></i>
             Download PDF
             </a>
           </li>
           <li>
-            <a class="dropdown-item sidebar-tools-main-item" href="./Machine-Learning-Systems-for-TinyML.epub">
+            <a class="dropdown-item sidebar-tools-main-item" href="./MACHINE-LEARNING-SYSTEMS-for-TinyML.epub">
               <i class="bi bi-bi-journal pe-1"></i>
             Download ePub
             </a>
@@ -382,6 +382,12 @@ <h1 class="title">Appendix F: Case Studies</h1>
 
   </div>
 
+<div>
+  <div class="abstract">
+    <div class="abstract-title">Abstract</div>
+    Machine Learning Systems for TinyML offers comprehensive guidance on deploying machine learning on embedded devices. As edge computing and the Internet of Things proliferate, this textbook provides professionals and students the expertise to implement performant AI on resource-constrained hardware. A unique aspect of this book elucidates the entire machine learning workflow, from data engineering through training, optimization, acceleration, and production deployment. Key topics covered include deep learning and classical ML algorithms for embedded systems, efficient neural network architectures, hardware-aware training techniques, model compression, benchmarking for tinyML, and on-device learning. Additional chapters highlight cutting-edge advances like on-device data generation and crucial considerations around reliability, privacy, security, and responsible AI. With its rigorous approach spanning theory and practice across diverse tinyML application domains like smart homes, wearables, and industrial IoT, the book enables readers to develop specialized knowledge. Using concrete use cases and hands-on examples, readers will learn to apply machine learning to transform embedded and IoT systems. Overall, this indispensable guide provides a research-based foundation for leveraging machine learning in embedded systems.
+  </div>
+</div>
 
 </header>