Merge branch 'dev'

_quarto.yml

@@ -4,15 +4,15 @@ project:
   preview:
     browser: true
     navigate: true
-
+    
 website:
   announcement: 
     icon: star-half
     dismissable: true
     content: |
-      🚀 <b>Our mission. 1 ⭐ = 1 👩‍🎓 Learner</b>. Every star tells a story: learners gaining knowledge and supporters fueling our mission. Together, we're making a difference. Thank you for your support and happy holidays!  
-      🎓 [Nov 15] The <a href="https://www.edgeaifoundation.org/">EDGE AI Foundation</a> is <b>matching academic scholarship funds</b> for every new GitHub ⭐ (up to 10,000 stars). <a href="https://github.com/harvard-edge/cs249r_book">Click here to show support!</a> 🙏  
-      📘 [Dec 22] <b>Chapter 3 updated!</b> New revisions include expanded content and improved explanations. Check it out <a href="https://mlsysbook.ai/contents/core/dl_primer/dl_primer.html">here</a>. 🌟
+      📘 <b>What's new?</b> 🎉 Happy New Year 2025! Chapters 1–5 expanded and improved, with a brand new Chapter 4!</br>
+      🚀 <b>Every learner deserves to be a ⭐</b> 1 GitHub ⭐ = 1 👩‍🎓 Learner. Your support fuels free, impactful educational resources on AI.</br>
+      🙏 <b>Thank you for your support!</b> Every GitHub star shows a learner engaging or a supporter driving our mission. You can click <a href="https://github.com/harvard-edge/cs249r_book">here</a> to star us.
 
     position: below-navbar
 
@@ -100,12 +100,12 @@ book:
     - text: "---"
     - contents/frontmatter/ai/socratiq.qmd
     - text: "---"
-    - part: "<div class=\"part\">PART I: Essentials</div>"
+    - part: "<div class=\"part\">The Essentials</div>"
     - contents/core/introduction/introduction.qmd
     - contents/core/ml_systems/ml_systems.qmd
     - contents/core/dl_primer/dl_primer.qmd
     - contents/core/dnn_architectures/dnn_architectures.qmd
-    - part: "<div class=\"part\">PART II: Principles</div>"
+    - part: "<div class=\"part\">Engineering Principles</div>"
     - contents/core/workflow/workflow.qmd
     - contents/core/data_engineering/data_engineering.qmd
     - contents/core/frameworks/frameworks.qmd
@@ -114,16 +114,16 @@ book:
     - contents/core/optimizations/optimizations.qmd
     - contents/core/hw_acceleration/hw_acceleration.qmd
     - contents/core/benchmarking/benchmarking.qmd
-    - contents/core/ondevice_learning/ondevice_learning.qmd
     - contents/core/ops/ops.qmd
-    - part: "<div class=\"part\">PART III: Practices</div>"
+    - part: "<div class=\"part\">Best Practices in AI</div>"
+    - contents/core/ondevice_learning/ondevice_learning.qmd
     - contents/core/privacy_security/privacy_security.qmd
     - contents/core/responsible_ai/responsible_ai.qmd
     - contents/core/sustainable_ai/sustainable_ai.qmd
     - contents/core/robust_ai/robust_ai.qmd
-    - contents/core/generative_ai/generative_ai.qmd
+#    - contents/core/generative_ai/generative_ai.qmd
+    - part: "<div class=\"part\">Closing Perspectives</div>"
     - contents/core/ai_for_good/ai_for_good.qmd
-    - part: "<div class=\"part\">PART IV: Closing</div>"
     - contents/core/conclusion/conclusion.qmd
     - text: "---"  
     - part: contents/labs/labs.qmd

contents/frontmatter/about/about.qmd

@@ -64,10 +64,10 @@ The book is organized into four main parts, each building on the previous one:
 2. **Engineering Principles (Chapters 5-13)**  
    Covers workflows, data engineering, optimization strategies, and operational challenges in system design.
 
-3. **AI in Practice (Chapters 14-20)**  
+3. **AI Best Practice (Chapters 14-18)**  
    Focuses on key considerations for deploying AI systems in real-world environments, including security, privacy, robustness, and sustainability.
 
-4. **Closing Perspectives (Chapter 21)**  
+4. **Closing Perspectives (Chapter 19-20)**  
    Synthesizes key lessons and explores emerging trends shaping the future of ML systems.
 
 ### Suggested Reading Paths

index.qmd

@@ -11,18 +11,18 @@ We've created this open-source book to demystify the process of building efficie
 > "If you want to go fast, go alone. If you want to go far, go together."
 > -- African Proverb
 
-This book connects foundational principles with practical workflows, guiding you through concepts like data collection, model design, optimization, acceleration, and security hardening, all from a systems perspective. Whether you're a student, practitioner, or researcher, we hope you find value in this dynamic, community-driven resource.
+As a living and breathing resource, this book is a continual work in progress, reflecting the ever-evolving nature of machine learning systems. Advancements in the ML landscape drive our commitment to keeping this resource updated with the latest insights, techniques, and best practices. We warmly invite you to join us on this journey by contributing your expertise, feedback, and ideas.
 
 ## Why We Wrote This Book
 
-In a world where technology is constantly evolving, open collaboration and shared knowledge are essential for innovation. This book goes beyond the traditional textbook model to serve as a living knowledge hub, inviting contributions and fostering a global community of learners and innovators.
+While there are plenty of resources that focus on the algorithmic side of machine learning, resources on the systems side of things are few and far between. This gap inspired us to create this book—a resource dedicated to the principles and practices of building efficient and scalable ML systems.
 
-By focusing on the principles and practices of ML systems, we aim to help you navigate the complexities of deploying and maintaining AI solutions. Together, let's explore the potential of machine learning to transform industries and improve lives.
+Our vision for this book and its broader mission is deeply rooted in the transformative potential of AI and the need to make AI education globally accessible to all. To learn more about the inspiration behind this project and the values driving its creation, we encourage you to read the [Author's Note](contents/frontmatter/foreword.qmd).
 
 ## Want to Help Out?
 
 This is a collaborative project, and your input matters! If you'd like to contribute, check out our [contribution guidelines](https://github.com/harvard-edge/cs249r_book/blob/dev/contribute.md). Feedback, corrections, and new ideas are welcome---simply file a GitHub [issue](https://github.com/harvard-edge/cs249r_book/issues).
 
 ## What's Next?
 
-If you're ready to dive deeper into the book's structure, learning objectives, and practical use, visit the [About the Book](contents/core/about/about.qmd#about-the-book-unnumbered) section for more details.
+If you're ready to dive deeper into the book's structure, learning objectives, and practical use, visit the [About the Book](contents/frontmatter/about/about.qmd#  about-the-book-unnumbered) section for more details.