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Fix 40 stale appendix title references across 33 files
Updated old appendix titles (e.g. "HuggingFace Ecosystem" to "HuggingFace: Transformers, Datasets, and Hub") in cross-references throughout appendices, pathways, syllabi, and chapter sections to match the renamed appendix structure. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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appendices/appendix-a-mathematical-foundations/section-a.5.html

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<p>Continue to <a class="cross-ref" href="../appendix-b-ml-essentials/index.html">Appendix B: ML Essentials Quick Reference</a> for the next reference appendix in this collection.</p>
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<p>Continue to <a class="cross-ref" href="../appendix-b-ml-essentials/index.html">Appendix B: Machine Learning Essentials</a> for the next reference appendix in this collection.</p>
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appendices/appendix-b-ml-essentials/section-b.4.html

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<p>Continue to <a class="cross-ref" href="../appendix-c-python-for-llm/index.html">Appendix C: Python for LLM Practitioners</a> for the next reference appendix in this collection.</p>
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<p>Continue to <a class="cross-ref" href="../appendix-c-python-for-llm/index.html">Appendix C: Python Libraries and Patterns for LLM Development</a> for the next reference appendix in this collection.</p>
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appendices/appendix-c-python-for-llm/index.html

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<p>This appendix serves readers who have Python experience from other domains (web development, data science, scripting) but have not yet worked in the deep learning or LLM space. It also serves as a cheat sheet for experienced practitioners who want a condensed reference.</p>
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<p>The libraries covered here are used throughout the book, with the heaviest usage in <a class="cross-ref" href="../../part-3-working-with-llms/module-10-llm-apis/index.html">Chapter 10 (LLM APIs)</a> and <a class="cross-ref" href="../../part-3-working-with-llms/module-11-prompt-engineering/index.html">Chapter 11 (Prompt Engineering)</a>. Fine-tuning workflows in <a class="cross-ref" href="../../part-4-training-adapting/module-14-fine-tuning-fundamentals/index.html">Chapter 14</a> rely on the environment and dependency patterns introduced here. For the HuggingFace-specific layer on top of these foundations, see <a class="cross-ref" href="../appendix-k-huggingface-ecosystem/index.html">Appendix K (HuggingFace Ecosystem)</a>.</p>
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<p>The libraries covered here are used throughout the book, with the heaviest usage in <a class="cross-ref" href="../../part-3-working-with-llms/module-10-llm-apis/index.html">Chapter 10 (LLM APIs)</a> and <a class="cross-ref" href="../../part-3-working-with-llms/module-11-prompt-engineering/index.html">Chapter 11 (Prompt Engineering)</a>. Fine-tuning workflows in <a class="cross-ref" href="../../part-4-training-adapting/module-14-fine-tuning-fundamentals/index.html">Chapter 14</a> rely on the environment and dependency patterns introduced here. For the HuggingFace-specific layer on top of these foundations, see <a class="cross-ref" href="../appendix-k-huggingface-ecosystem/index.html">Appendix K (HuggingFace: Transformers, Datasets, and Hub)</a>.</p>
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appendices/appendix-c-python-for-llm/section-c.4.html

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<p>Continue to <a class="cross-ref" href="../appendix-d-environment-setup/index.html">Appendix D: Environment Setup Guide</a> for the next reference appendix in this collection.</p>
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<p>This appendix is essential for readers setting up a new machine or cloud instance. It is also useful for debugging mysterious failures in code that should work. Experienced ML engineers who already have a functional environment can skip to Section D.6 to verify their setup meets the requirements for this course.</p>
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<p>Once your environment is configured, the Python libraries it hosts are covered in <a class="cross-ref" href="../appendix-c-python-for-llm/index.html">Appendix C (Python for LLM Practitioners)</a>. Hardware selection decisions are informed by <a class="cross-ref" href="../appendix-g-hardware-compute/index.html">Appendix G (Hardware and Compute Reference)</a>, which covers GPU specs and cloud pricing. Fine-tuning workflows that have specific environment requirements are in <a class="cross-ref" href="../../part-4-training-adapting/module-14-fine-tuning-fundamentals/index.html">Chapter 14</a> and <a class="cross-ref" href="../../part-4-training-adapting/module-15-peft/index.html">Chapter 15</a>.</p>
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<p>Once your environment is configured, the Python libraries it hosts are covered in <a class="cross-ref" href="../appendix-c-python-for-llm/index.html">Appendix C (Python Libraries and Patterns for LLM Development)</a>. Hardware selection decisions are informed by <a class="cross-ref" href="../appendix-g-hardware-compute/index.html">Appendix G (GPU Hardware and Cloud Compute)</a>, which covers GPU specs and cloud pricing. Fine-tuning workflows that have specific environment requirements are in <a class="cross-ref" href="../../part-4-training-adapting/module-14-fine-tuning-fundamentals/index.html">Chapter 14</a> and <a class="cross-ref" href="../../part-4-training-adapting/module-15-peft/index.html">Chapter 15</a>.</p>
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<p>Continue to <a class="cross-ref" href="../appendix-e-git-collaboration/index.html">Appendix E: Git and Collaboration for ML Projects</a> for the next reference appendix in this collection.</p>
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<p>Continue to <a class="cross-ref" href="../appendix-e-git-collaboration/index.html">Appendix E: Git, DVC, and Reproducibility</a> for the next reference appendix in this collection.</p>
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