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work flow to convert ipynb to qmd
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‎.github/workflows/convert-to-qmd.yml

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name: Convert Notebooks and Update Book
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on:
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push:
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paths:
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- 'notebooks/**.ipynb'
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jobs:
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convert-and-copy:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout the Linear_Algebra_with_Python repository
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uses: actions/[email protected]
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- name: Install Quarto
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run: |
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wget https://github.com/quarto-dev/quarto-cli/releases/download/v1.5.56/quarto-1.5.56-linux-amd64.tar.gz
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tar -xvzf quarto-1.5.56-linux-amd64.tar.gz
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sudo cp -r quarto-1.5.56/* /usr/local/
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- name: Convert Notebooks to QMD
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run: |
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mkdir -p converted_chapters
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for notebook in notebooks/*.ipynb; do
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quarto convert "$notebook" --to qmd -o "converted_chapters/$(basename "$notebook" .ipynb).qmd"
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done
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- name: Checkout the weijie-chen.github.io repository
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uses: actions/[email protected]
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with:
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repository: weijie-chen/weijie-chen.github.io
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path: book_repo
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- name: Copy Converted QMD Files to Book Repository
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run: |
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mkdir -p book_repo/linear-algebra-with-python-book/chapters
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cp converted_chapters/*.qmd book_repo/linear-algebra-with-python-book/chapters/
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- name: Update Table of Contents in index.html
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run: |
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toc=""
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for chapter in book_repo/linear-algebra-with-python-book/chapters/*.qmd; do
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chapter_name=$(basename "$chapter" .qmd)
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toc="$toc<li>$chapter_name</li>\n"
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done
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sed -i '/<section class="table-of-contents">/,/<\/section>/{//!d}' book_repo/linear-algebra-with-python-book/index.html
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sed -i "/<section class=\"table-of-contents\">/a $toc" book_repo/linear-algebra-with-python-book/index.html
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- name: Commit and Push Changes
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run: |
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cd book_repo
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git config user.name "GitHub Actions"
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git config user.email "[email protected]"
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git add linear-algebra-with-python-book/chapters/*
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git add linear-algebra-with-python-book/index.html
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git commit -m "Updated chapters and Table of Contents"
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git push

‎index.html

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<hr>
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<main>
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<article>
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<!-- <div class="language-link">
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<a href="es.html">- Español -</a>
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</div> -->
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<p> <i>Linear Algebra with Python</i> is designed to
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provide a comprehensive
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refresher on the essential concepts of linear
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algebra, tailored for professionals and students in
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fields such as statistics, econometrics,
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quantitative analysis, and data science. Whether
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you're a seasoned expert or someone looking to
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solidify your foundational knowledge, this guide
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offers an in-depth exploration of critical topics in
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linear algebra, enriched with Python-based
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computation and visualization.</p>
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<p>Throughout this book, we will delve into key concepts
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that are pivotal for advanced quantitative skill
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sets, including linear combinations, vector spaces,
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linear transformations, eigenvalues and
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eigenvectors, diagonalization, singular value
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decomposition, and more. Each concept is carefully
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explained and demonstrated with Python, making
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abstract ideas more concrete and applicable to
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real-world scenarios.</p>
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<p>Ready to get started? Skip ahead to the <a
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href="content/table_of_contents.html">Table of
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Contents.</a>
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</p>
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<article>
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<!-- <div class="language-link">
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<a href="es.html">- Español -</a>
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</div> -->
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<p> <i>Linear Algebra with Python</i> is designed to
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provide a comprehensive
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refresher on the essential concepts of linear
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algebra, tailored for professionals and students in
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fields such as statistics, econometrics,
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quantitative analysis, and data science. Whether
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you're a seasoned expert or someone looking to
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solidify your foundational knowledge, this guide
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offers an in-depth exploration of critical topics in
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linear algebra, enriched with Python-based
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computation and visualization.</p>
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<p>Throughout this book, we will delve into key concepts
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that are pivotal for advanced quantitative skill
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sets, including linear combinations, vector spaces,
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linear transformations, eigenvalues and
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eigenvectors, diagonalization, singular value
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decomposition, and more. Each concept is carefully
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explained and demonstrated with Python, making
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abstract ideas more concrete and applicable to
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real-world scenarios.</p>
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<p>Ready to get started? Skip ahead to the <a
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href="content/table_of_contents.html">Table of
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Contents.</a>
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</p>
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<hr>
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<hr>
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<h2 style="text-align: center;">WHAT IT IS ABOUT</h2>
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<h2 style="text-align: center;">WHAT IT IS ABOUT</h2>
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<div
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style=" text-wrap: wrap; display: flex; justify-content: center;">

‎linear-algebra-with-python-book/css/styles.css

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cursor: pointer;
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}
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/* Article styling */
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/* introduction */
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.introduction {
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display: flex;
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flex-direction: column;
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color: var(--text-color);
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background-color: var(--second-bg-color);
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border-radius: 10px;
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}
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/* Smooth transition for fixed elements */
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#book-cover {
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transition: top 0.5s ease-in-out;
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}
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margin: 20px auto 0 auto; /* Center the section horizontally */
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max-width: 900px; /* Limit the width of the section */
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line-height: 1.6;
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}

‎linear-algebra-with-python-book/index.html

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</section>
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<section class="introduction">
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<!-- <div class="language-link">
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<a href="es.html">- Español -</a>
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</div> -->
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<h2>INTRODUCTION</h2>
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<br>
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<br>
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<p> <i>Linear Algebra with Python</i> is designed to
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provide a comprehensive
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refresher on the essential concepts of linear
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offers an in-depth exploration of critical topics in
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linear algebra, enriched with Python-based
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computation and visualization.</p>
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<br>
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<p>Throughout this book, we will delve into key concepts
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that are pivotal for advanced quantitative skill
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sets, including linear combinations, vector spaces,
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abstract ideas more concrete and applicable to
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real-world scenarios.</p>
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<p>Ready to get started? Skip ahead to the <a
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href="content/table_of_contents.html">Table of
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Contents.</a>
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</p>
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<hr>
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</section>
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<h2 style="text-align: center;">WHAT IT IS ABOUT</h2>
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</section>
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<section class="table-of-contents">
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<h2>TABLE OF CONTENTS</h2>
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<br>
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<br>
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<!-- <ul>
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<li>Chapter 1: Introduction to Linear Algebra</li>
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<li>Chapter 2: Linear Combinations and Span</li>
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<li>Chapter 3: Vector Spaces</li>
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<li>Chapter 4: Linear Transformations</li>
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<li>Chapter 5: Eigenvalues and Eigenvectors</li>
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<li>Chapter 6: Diagonalization</li>
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<li>Chapter 7: Singular Value Decomposition</li>
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<li>Chapter 8: Applications of Linear Algebra</li>
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</ul> -->
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<hr>
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</section>
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