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docs: convert all README markdown tables to HTML format
Standardize table formatting across 25 README files to use HTML tables with consistent styling (thead/tbody, column widths, bold labels) matching the main README's presentation.
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book/README.md

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@@ -21,50 +21,163 @@ This directory contains the textbook source and build system for contributors.
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## What You Will Learn
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| ML Concepts | Systems Engineering |
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|-------------|---------------------|
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| Neural networks and deep learning | Memory hierarchies and caching |
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| Model architectures (CNNs, Transformers) | Hardware accelerators (GPUs, TPUs, NPUs) |
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| Training and optimization | Distributed systems and parallelism |
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| Inference and deployment | Power and thermal management |
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| Compression and quantization | Latency, throughput, and efficiency |
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<table>
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<thead>
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<tr>
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<th width="50%">ML Concepts</th>
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<th width="50%">Systems Engineering</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>Neural networks and deep learning</td>
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<td>Memory hierarchies and caching</td>
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</tr>
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<tr>
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<td>Model architectures (CNNs, Transformers)</td>
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<td>Hardware accelerators (GPUs, TPUs, NPUs)</td>
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</tr>
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<tr>
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<td>Training and optimization</td>
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<td>Distributed systems and parallelism</td>
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</tr>
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<tr>
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<td>Inference and deployment</td>
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<td>Power and thermal management</td>
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</tr>
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<tr>
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<td>Compression and quantization</td>
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<td>Latency, throughput, and efficiency</td>
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</tr>
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</tbody>
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</table>
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### The ML ↔ Systems Bridge
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| You know... | You will learn... |
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|-------------|-------------------|
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| How to train a model | How training scales across GPU clusters |
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| That quantization shrinks models | How INT8 math maps to silicon |
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| What a transformer is | Why KV-cache dominates memory |
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| Models run on GPUs | How schedulers balance latency vs throughput |
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| Edge devices have limits | How to co-design models and hardware |
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<table>
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<thead>
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<tr>
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<th width="50%">You know...</th>
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<th width="50%">You will learn...</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>How to train a model</td>
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<td>How training scales across GPU clusters</td>
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</tr>
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<tr>
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<td>That quantization shrinks models</td>
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<td>How INT8 math maps to silicon</td>
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</tr>
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<tr>
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<td>What a transformer is</td>
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<td>Why KV-cache dominates memory</td>
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</tr>
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<tr>
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<td>Models run on GPUs</td>
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<td>How schedulers balance latency vs throughput</td>
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</tr>
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<tr>
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<td>Edge devices have limits</td>
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<td>How to co-design models and hardware</td>
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</tr>
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</tbody>
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</table>
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### Book Structure
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This textbook is organized into **two volumes** following the Hennessy & Patterson pedagogical model:
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| Volume | Theme | Focus |
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|--------|-------|-------|
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| **Volume I** | Build, Optimize, Deploy | Single-machine ML systems, foundational principles |
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| **Volume II** | Scale, Distribute, Govern | Distributed systems at production scale |
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<table>
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<thead>
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<tr>
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<th width="20%">Volume</th>
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<th width="30%">Theme</th>
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<th width="50%">Focus</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td><b>Volume I</b></td>
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<td>Build, Optimize, Deploy</td>
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<td>Single-machine ML systems, foundational principles</td>
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</tr>
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<tr>
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<td><b>Volume II</b></td>
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<td>Scale, Distribute, Govern</td>
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<td>Distributed systems at production scale</td>
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</tr>
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</tbody>
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</table>
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#### Volume I: Build, Optimize, Deploy
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| Part | Focus | Chapters |
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|------|-------|----------|
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| **Foundations** | Core concepts | Introduction, ML Systems, DL Primer, Architectures |
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| **Development** | Building blocks | Workflow, Data Engineering, Frameworks, Training |
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| **Optimization** | Making it fast | Efficient AI, Optimizations, HW Acceleration, Benchmarking |
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| **Deployment** | Making it work | Serving, MLOps, Responsible Engineering |
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<table>
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<thead>
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<tr>
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<th width="20%">Part</th>
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<th width="25%">Focus</th>
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<th width="55%">Chapters</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td><b>Foundations</b></td>
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<td>Core concepts</td>
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<td>Introduction, ML Systems, DL Primer, Architectures</td>
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</tr>
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<tr>
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<td><b>Development</b></td>
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<td>Building blocks</td>
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<td>Workflow, Data Engineering, Frameworks, Training</td>
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</tr>
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<tr>
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<td><b>Optimization</b></td>
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<td>Making it fast</td>
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<td>Efficient AI, Optimizations, HW Acceleration, Benchmarking</td>
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</tr>
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<tr>
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<td><b>Deployment</b></td>
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<td>Making it work</td>
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<td>Serving, MLOps, Responsible Engineering</td>
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</tr>
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</tbody>
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</table>
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#### Volume II: Scale, Distribute, Govern
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| Part | Focus | Chapters |
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|------|-------|----------|
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| **Foundations of Scale** | Infrastructure | Infrastructure, Storage, Communication |
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| **Distributed Systems** | Coordination | Distributed Training, Fault Tolerance, Inference, Edge Intelligence |
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| **Production Challenges** | Operations | On-device Learning, Privacy & Security, Robust AI, Ops at Scale |
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| **Responsible Deployment** | Trust | Responsible AI, Sustainable AI, AI for Good, Frontiers |
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<table>
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<thead>
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<tr>
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<th width="25%">Part</th>
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<th width="20%">Focus</th>
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<th width="55%">Chapters</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td><b>Foundations of Scale</b></td>
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<td>Infrastructure</td>
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<td>Infrastructure, Storage, Communication</td>
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</tr>
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<tr>
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<td><b>Distributed Systems</b></td>
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<td>Coordination</td>
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<td>Distributed Training, Fault Tolerance, Inference, Edge Intelligence</td>
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</tr>
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<tr>
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<td><b>Production Challenges</b></td>
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<td>Operations</td>
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<td>On-device Learning, Privacy & Security, Robust AI, Ops at Scale</td>
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</tr>
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<tr>
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<td><b>Responsible Deployment</b></td>
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<td>Trust</td>
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<td>Responsible AI, Sustainable AI, AI for Good, Frontiers</td>
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</tr>
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</tbody>
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</table>
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---
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## Documentation
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| Audience | Resources |
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|----------|-----------|
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| **Readers** | [Online Book](https://mlsysbook.ai/book/)[Volume I](https://mlsysbook.ai/vol1/)[Volume II](https://mlsysbook.ai/vol2/)[PDF](https://mlsysbook.ai/book/assets/downloads/Machine-Learning-Systems.pdf) |
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| **Contributors** | [CONTRIBUTING.md](docs/CONTRIBUTING.md)[BUILD.md](docs/BUILD.md) |
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| **Developers** | [DEVELOPMENT.md](docs/DEVELOPMENT.md)[BINDER.md](docs/BINDER.md) |
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<table>
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<thead>
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<tr>
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<th width="20%">Audience</th>
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<th width="80%">Resources</th>
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</thead>
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<tbody>
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<tr>
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<td><b>Readers</b></td>
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<td><a href="https://mlsysbook.ai/book/">Online Book</a> ・ <a href="https://mlsysbook.ai/vol1/">Volume I</a> ・ <a href="https://mlsysbook.ai/vol2/">Volume II</a> ・ <a href="https://mlsysbook.ai/book/assets/downloads/Machine-Learning-Systems.pdf">PDF</a></td>
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</tr>
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<td><b>Contributors</b></td>
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<td><a href="docs/CONTRIBUTING.md">CONTRIBUTING.md</a> ・ <a href="docs/BUILD.md">BUILD.md</a></td>
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</tr>
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<td><b>Developers</b></td>
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<td><a href="docs/DEVELOPMENT.md">DEVELOPMENT.md</a> ・ <a href="docs/BINDER.md">BINDER.md</a></td>
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</tr>
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</tbody>
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</table>
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Binder is the public automation API for book build/validate/maintenance workflows.
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Use Binder subcommands in editor integrations and CI where possible.
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## Related
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| Component | Description |
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|-----------|-------------|
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| **[Main README](../README.md)** | Project overview and ecosystem |
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| **[TinyTorch](../tinytorch/)** | Build ML frameworks from scratch |
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| **[Hardware Kits](../kits/)** | Deploy to Arduino, Raspberry Pi, edge devices |
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| **[Website](https://mlsysbook.ai/book/)** | Read the book online |
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<table>
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<thead>
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<th width="30%">Component</th>
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<th width="70%">Description</th>
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</thead>
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<tbody>
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<td><b><a href="../README.md">Main README</a></b></td>
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<td>Project overview and ecosystem</td>
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<td><b><a href="../tinytorch/">TinyTorch</a></b></td>
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<td>Build ML frameworks from scratch</td>
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</tr>
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<td><b><a href="../kits/">Hardware Kits</a></b></td>
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<td>Deploy to Arduino, Raspberry Pi, edge devices</td>
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</tr>
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<td><b><a href="https://mlsysbook.ai/book/">Website</a></b></td>
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<td>Read the book online</td>
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</tr>
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</tbody>
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</table>
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---
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book/quarto/_extensions/README.md

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### 📋 Extension Inventory
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| Extension | Status | Safe to Update? |
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|-----------|--------|-----------------|
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| `margin-video` | 🚫 **100% Custom** | Never (MLSysBook only) |
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| `ute/custom-numbered-blocks` | 🚫 **Heavy Customization** | Never |
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| `nmfs-opensci/titlepage` | 🚫 **Customized** | Never |
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| `pandoc-ext/diagram` | ✅ Standard | Yes |
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| `quarto-ext/lightbox` | ✅ Standard | Yes |
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<thead>
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<th width="40%"><b>Extension</b></th>
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<th width="35%">Status</th>
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<th width="25%">Safe to Update?</th>
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</tr>
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</thead>
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<tbody>
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<tr><td><b>`margin-video`</b></td><td>🚫 **100% Custom**</td><td>Never (MLSysBook only)</td></tr>
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<tr><td><b>`ute/custom-numbered-blocks`</b></td><td>🚫 **Heavy Customization**</td><td>Never</td></tr>
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<tr><td><b>`nmfs-opensci/titlepage`</b></td><td>🚫 **Customized**</td><td>Never</td></tr>
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<tr><td><b>`pandoc-ext/diagram`</b></td><td>✅ Standard</td><td>Yes</td></tr>
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<tr><td><b>`quarto-ext/lightbox`</b></td><td>✅ Standard</td><td>Yes</td></tr>
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</tbody>
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</table>
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### 🚨 Emergency Recovery
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book/quarto/_extensions/mlsysbook-ext/margin-video/README.md

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## Supported Options
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| Option | Description | Default | Example |
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|--------|-------------|---------|---------|
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| `aspect-ratio` | Video aspect ratio | `"16/9"` | `aspect-ratio="4/3"` |
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| `start` | Start time in seconds | none | `start="120"` |
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| `autoplay` | Enable autoplay | `false` | `autoplay="true"` |
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<th width="20%"><b>Option</b></th>
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<th width="35%">Description</th>
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<th width="15%">Default</th>
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<th width="30%">Example</th>
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</thead>
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<tr><td><b>`aspect-ratio`</b></td><td>Video aspect ratio</td><td>`"16/9"`</td><td>`aspect-ratio="4/3"`</td></tr>
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<tr><td><b>`start`</b></td><td>Start time in seconds</td><td>none</td><td>`start="120"`</td></tr>
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<tr><td><b>`autoplay`</b></td><td>Enable autoplay</td><td>`false`</td><td>`autoplay="true"`</td></tr>
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</tbody>
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## Features
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book/quarto/contents/README.md

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## Directory structure
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| Path | Purpose |
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|------|---------|
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| **`shared/`** | Content used by **both** Volume I and Volume II (e.g. notation, conventions). |
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| **`frontmatter/`** | Site-level front matter (about, acknowledgements, Socratiq, etc.). |
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| **`index.qmd`** | Root book index (under `frontmatter/` in some configs). |
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| **`vol1/`** | Volume I: Introduction to Machine Learning Systems. |
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| **`vol2/`** | Volume II: Machine Learning Systems at Scale. |
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<thead>
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<th width="25%">Path</th>
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<th width="75%">Purpose</th>
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<td><b><code>shared/</code></b></td>
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<td>Content used by <b>both</b> Volume I and Volume II (e.g. notation, conventions).</td>
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</tr>
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<td><b><code>frontmatter/</code></b></td>
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<td>Site-level front matter (about, acknowledgements, Socratiq, etc.).</td>
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</tr>
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<td><b><code>index.qmd</code></b></td>
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<td>Root book index (under <code>frontmatter/</code> in some configs).</td>
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<td><b><code>vol1/</code></b></td>
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<td>Volume I: Introduction to Machine Learning Systems.</td>
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</tr>
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<td><b><code>vol2/</code></b></td>
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<td>Volume II: Machine Learning Systems at Scale.</td>
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</tr>
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</tbody>
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</table>
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## Required non-volume paths
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