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- Reorganized chapter structure with new numbering system - Added new chapters: introduction, tokenization, embeddings, profiling, quantization, caching - Removed obsolete chapters (15-mlops) and consolidated content - Updated table of contents and navigation structure - Enhanced visual design with new logos and favicon - Added comprehensive documentation (FAQ, user manual, command reference, competitions) - Improved theme design and custom CSS styling - Added QUICKSTART.md for rapid onboarding - Updated all chapter cross-references and links
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TinyTorch: Build ML Systems from Scratch
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<h1>📚 Additional Learning Resources</h1>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#academic-courses">🎓 Academic Courses</a><ul class="nav section-nav flex-column">
|
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#machine-learning-systems">Machine Learning Systems</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#deep-learning-foundations">Deep Learning Foundations</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#recommended-books">📖 Recommended Books</a><ul class="nav section-nav flex-column">
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#implementation-theory">Implementation & Theory</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#alternative-implementations">🛠️ Alternative Implementations</a><ul class="nav section-nav flex-column">
|
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#minimal-frameworks">Minimal Frameworks</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#framework-deep-dives">Framework Deep Dives</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#ready-to-begin-your-journey">🚀 Ready to Begin Your Journey?</a></li>
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<section id="additional-learning-resources">
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<h1>📚 Additional Learning Resources<a class="headerlink" href="#additional-learning-resources" title="Link to this heading">#</a></h1>
|
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<div style="background: #f8f9fa; border: 1px solid #dee2e6; padding: 2rem; border-radius: 0.5rem; text-align: center; margin: 2rem 0;">
|
||
<h2 style="margin: 0 0 1rem 0; color: #495057;">Complement Your TinyTorch Journey</h2>
|
||
<p style="margin: 0; color: #6c757d;">Carefully selected resources for broader context, alternative perspectives, and production tools</p>
|
||
</div>
|
||
<p>While TinyTorch teaches you to build complete ML systems from scratch, these resources provide broader context, alternative perspectives, and production tools.</p>
|
||
<p><strong>TinyTorch Learning Resources:</strong></p>
|
||
<ul class="simple">
|
||
<li><p><strong><a class="reference internal" href="#learning-timeline.html"><span class="xref myst">Your Learning Journey →</span></a></strong> - Start here to understand the 5-stage progression</p></li>
|
||
<li><p><strong><a class="reference internal" href="#checkpoint-system.html"><span class="xref myst">Progress Tracking →</span></a></strong> - Technical details on capability testing</p></li>
|
||
<li><p><strong><a class="reference internal" href="#testing-framework.html"><span class="xref myst">Testing Guide →</span></a></strong> - Comprehensive testing methodology</p></li>
|
||
<li><p><strong><a class="reference internal" href="#leaderboard.html"><span class="xref myst">Achievement Showcase →</span></a></strong> - Portfolio development and career readiness</p></li>
|
||
</ul>
|
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<hr class="docutils" />
|
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<section id="academic-courses">
|
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<h2>🎓 Academic Courses<a class="headerlink" href="#academic-courses" title="Link to this heading">#</a></h2>
|
||
<section id="machine-learning-systems">
|
||
<h3>Machine Learning Systems<a class="headerlink" href="#machine-learning-systems" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong><a class="reference external" href="https://stanford-cs329s.github.io/">CS 329S: Machine Learning Systems Design</a></strong> (Stanford)<br />
|
||
<em>Production ML systems, infrastructure, and deployment at scale</em></p></li>
|
||
<li><p><strong><a class="reference external" href="https://hanlab.mit.edu/courses/2024-fall-65940">CS 6.S965: TinyML and Efficient Deep Learning</a></strong> (MIT)<br />
|
||
<em>Edge computing, model compression, and efficient ML algorithms</em></p></li>
|
||
<li><p><strong><a class="reference external" href="https://sites.google.com/g.harvard.edu/tinyml/home">CS 249r: Tiny Machine Learning</a></strong> (Harvard)<br />
|
||
<em>TinyML systems, edge AI, and resource-constrained machine learning</em></p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="deep-learning-foundations">
|
||
<h3>Deep Learning Foundations<a class="headerlink" href="#deep-learning-foundations" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong><a class="reference external" href="http://cs231n.stanford.edu/">CS 231n: Convolutional Neural Networks</a></strong> (Stanford)<br />
|
||
<em>Computer vision and CNN architectures - complements TinyTorch spatial modules</em></p></li>
|
||
<li><p><strong><a class="reference external" href="http://web.stanford.edu/class/cs224n/">CS 224n: Natural Language Processing</a></strong> (Stanford)<br />
|
||
<em>NLP and transformers - perfect follow-up to TinyTorch attention module</em></p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="recommended-books">
|
||
<h2>📖 Recommended Books<a class="headerlink" href="#recommended-books" title="Link to this heading">#</a></h2>
|
||
<section id="systems-engineering">
|
||
<h3>Systems & Engineering<a class="headerlink" href="#systems-engineering" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong><a class="reference external" href="https://mlsysbook.ai">Machine Learning Systems</a></strong> by Prof. Vijay Janapa Reddi (Harvard)<br />
|
||
<em>Comprehensive systems perspective on ML engineering and optimization - the perfect companion to TinyTorch</em></p></li>
|
||
<li><p><strong><a class="reference external" href="https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/">Designing Machine Learning Systems</a></strong> by Chip Huyen<br />
|
||
<em>Production ML engineering, data pipelines, and system design</em></p></li>
|
||
<li><p><strong><a class="reference external" href="https://www.mlebook.com/wiki/doku.php">Machine Learning Engineering</a></strong> by Andriy Burkov<br />
|
||
<em>End-to-end ML project lifecycle and best practices</em></p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="implementation-theory">
|
||
<h3>Implementation & Theory<a class="headerlink" href="#implementation-theory" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong><a class="reference external" href="https://www.deeplearningbook.org/">Deep Learning</a></strong> by Ian Goodfellow, Yoshua Bengio, Aaron Courville<br />
|
||
<em>Mathematical foundations - the theory behind what you implement in TinyTorch</em></p></li>
|
||
<li><p><strong><a class="reference external" href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/">Hands-On Machine Learning</a></strong> by Aurélien Géron<br />
|
||
<em>Practical implementations using established frameworks</em></p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="alternative-implementations">
|
||
<h2>🛠️ Alternative Implementations<a class="headerlink" href="#alternative-implementations" title="Link to this heading">#</a></h2>
|
||
<p><strong>Different approaches to building ML systems from scratch - see how others tackle the same challenge:</strong></p>
|
||
<section id="minimal-frameworks">
|
||
<h3>Minimal Frameworks<a class="headerlink" href="#minimal-frameworks" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong><a class="reference external" href="https://github.com/karpathy/micrograd">Micrograd</a></strong> by Andrej Karpathy<br />
|
||
<em>Minimal autograd engine in 100 lines. <strong>Micrograd shows you the math, TinyTorch shows you the systems.</strong></em></p></li>
|
||
<li><p><strong><a class="reference external" href="https://github.com/Kipre/microtorch">Microtorch</a></strong> by Kipre<br />
|
||
<em>PyTorch-like API in pure Python. <strong>Microtorch focuses on clean API design, TinyTorch emphasizes systems engineering and scalability.</strong></em></p></li>
|
||
<li><p><strong><a class="reference external" href="https://github.com/geohot/tinygrad">Tinygrad</a></strong> by George Hotz<br />
|
||
<em>Performance-focused educational framework. <strong>Tinygrad optimizes for speed, TinyTorch optimizes for learning.</strong></em></p></li>
|
||
<li><p><strong><a class="reference external" href="https://nnfs.io/">Neural Networks from Scratch</a></strong> by Harrison Kinsley<br />
|
||
<em>Math-heavy implementation approach. <strong>NNFS focuses on algorithms, TinyTorch focuses on systems engineering.</strong></em></p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="production-internals">
|
||
<h2>🏭 Production Internals<a class="headerlink" href="#production-internals" title="Link to this heading">#</a></h2>
|
||
<section id="framework-deep-dives">
|
||
<h3>Framework Deep Dives<a class="headerlink" href="#framework-deep-dives" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong><a class="reference external" href="http://blog.ezyang.com/2019/05/pytorch-internals/">PyTorch Internals</a></strong> by Edward Yang<br />
|
||
<em>How PyTorch actually works under the hood - a great read as see what you built in TinyTorch corresponds to the real PyTorch</em></p></li>
|
||
<li><p><strong><a class="reference external" href="https://pytorch.org/docs/stable/notes/extending.html">PyTorch Documentation: Extending PyTorch</a></strong><br />
|
||
<em>Custom operators and autograd functions - apply your TinyTorch knowledge</em></p></li>
|
||
</ul>
|
||
<hr class="docutils" />
|
||
<p><em>Building ML systems from scratch gives you the implementation foundation most ML engineers lack. These resources help you apply that knowledge to broader systems and production environments.</em></p>
|
||
</section>
|
||
</section>
|
||
<section id="ready-to-begin-your-journey">
|
||
<h2>🚀 Ready to Begin Your Journey?<a class="headerlink" href="#ready-to-begin-your-journey" title="Link to this heading">#</a></h2>
|
||
<p><strong>Start with the fundamentals and build your way up:</strong></p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Check your current progress</span>
|
||
tito<span class="w"> </span>checkpoint<span class="w"> </span>status
|
||
|
||
<span class="c1"># Begin your first module</span>
|
||
tito<span class="w"> </span>module<span class="w"> </span><span class="nb">complete</span><span class="w"> </span>01_setup
|
||
|
||
<span class="c1"># See your learning path</span>
|
||
tito<span class="w"> </span>checkpoint<span class="w"> </span>timeline
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>Your Next Steps:</strong></p>
|
||
<ol class="arabic simple">
|
||
<li><p><strong><a class="reference internal" href="#learning-timeline.html"><span class="xref myst">Understanding the Journey →</span></a></strong> - See the big picture of what you’ll build</p></li>
|
||
<li><p><strong><a class="reference internal" href="#../chapters/01-setup.html"><span class="xref myst">Start Building →</span></a></strong> - Configure environment and begin Module 1</p></li>
|
||
<li><p><strong><a class="reference internal" href="#checkpoint-system.html"><span class="xref myst">Track Progress →</span></a></strong> - Learn the capability testing system</p></li>
|
||
</ol>
|
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<div style="background: #f8f9fa; border: 1px solid #dee2e6; padding: 1.5rem; border-radius: 0.5rem; margin: 2rem 0; text-align: center;">
|
||
<h4 style="margin: 0 0 1rem 0; color: #495057;">🎯 Transform from Framework User to Systems Engineer</h4>
|
||
<p style="margin: 0; color: #6c757d;">These external resources complement the hands-on systems building you'll do in TinyTorch</p>
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</div> </section>
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