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TinyTorch: Build ML Systems from Scratch
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<h1>Additional Learning Resources</h1>
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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="Permalink 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;">
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<h2 style="margin: 0 0 1rem 0; color: #495057;">Complement Your TinyTorch Journey</h2>
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<p style="margin: 0; color: #6c757d;">Carefully selected resources for broader context, alternative perspectives, and production tools</p>
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</div>
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<p>While TinyTorch teaches you to build complete ML systems from scratch, these resources provide broader context, alternative perspectives, and production tools.</p>
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<p><strong>TinyTorch Learning Resources:</strong></p>
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<ul class="simple">
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<li><p>See <a class="reference internal" href="student-workflow.html"><span class="doc std std-doc">Student Workflow</span></a> for development workflow and progress tracking</p></li>
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<li><p>See <a class="reference internal" href="testing-framework.html"><span class="doc std std-doc">Testing Guide</span></a> for comprehensive testing methodology</p></li>
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</ul>
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<section id="academic-courses">
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<h2>Academic Courses<a class="headerlink" href="#academic-courses" title="Permalink to this heading">#</a></h2>
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<section id="machine-learning-systems">
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<h3>Machine Learning Systems<a class="headerlink" href="#machine-learning-systems" title="Permalink to this heading">#</a></h3>
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<ul class="simple">
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<li><p><strong><a class="reference external" href="https://stanford-cs329s.github.io/">CS 329S: Machine Learning Systems Design</a></strong> (Stanford)<br />
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<em>Production ML systems, infrastructure, and deployment at scale</em></p></li>
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<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 />
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<em>Edge computing, model compression, and efficient ML algorithms</em></p></li>
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<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 />
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<em>TinyML systems, edge AI, and resource-constrained machine learning</em></p></li>
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</ul>
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</section>
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<section id="deep-learning-foundations">
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<h3>Deep Learning Foundations<a class="headerlink" href="#deep-learning-foundations" title="Permalink to this heading">#</a></h3>
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<ul class="simple">
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<li><p><strong><a class="reference external" href="http://cs231n.stanford.edu/">CS 231n: Convolutional Neural Networks</a></strong> (Stanford)<br />
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<em>Computer vision and CNN architectures - complements TinyTorch spatial modules</em></p></li>
|
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<li><p><strong><a class="reference external" href="http://web.stanford.edu/class/cs224n/">CS 224n: Natural Language Processing</a></strong> (Stanford)<br />
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<em>NLP and transformers - perfect follow-up to TinyTorch attention module</em></p></li>
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</ul>
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</section>
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</section>
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<hr class="docutils" />
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<section id="recommended-books">
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<h2>Recommended Books<a class="headerlink" href="#recommended-books" title="Permalink to this heading">#</a></h2>
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<section id="systems-engineering">
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<h3>Systems & Engineering<a class="headerlink" href="#systems-engineering" title="Permalink to this heading">#</a></h3>
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<ul class="simple">
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<li><p><strong><a class="reference external" href="https://mlsysbook.ai">Machine Learning Systems</a></strong> by Prof. Vijay Janapa Reddi (Harvard)<br />
|
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<em>Comprehensive systems perspective on ML engineering and optimization - the perfect companion to TinyTorch</em></p></li>
|
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<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 />
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<em>Production ML engineering, data pipelines, and system design</em></p></li>
|
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<li><p><strong><a class="reference external" href="https://www.mlebook.com/wiki/doku.php">Machine Learning Engineering</a></strong> by Andriy Burkov<br />
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<em>End-to-end ML project lifecycle and best practices</em></p></li>
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</ul>
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</section>
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<section id="implementation-theory">
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<h3>Implementation & Theory<a class="headerlink" href="#implementation-theory" title="Permalink to this heading">#</a></h3>
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<ul class="simple">
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<li><p><strong><a class="reference external" href="https://www.deeplearningbook.org/">Deep Learning</a></strong> by Ian Goodfellow, Yoshua Bengio, Aaron Courville<br />
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<em>Mathematical foundations - the theory behind what you implement in TinyTorch</em></p></li>
|
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<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 />
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<em>Practical implementations using established frameworks</em></p></li>
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</ul>
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</section>
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</section>
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<section id="alternative-implementations">
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<h2>Alternative Implementations<a class="headerlink" href="#alternative-implementations" title="Permalink to this heading">#</a></h2>
|
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<p><strong>Different approaches to building ML systems from scratch - see how others tackle the same challenge:</strong></p>
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<section id="minimal-frameworks">
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<h3>Minimal Frameworks<a class="headerlink" href="#minimal-frameworks" title="Permalink to this heading">#</a></h3>
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<ul class="simple">
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<li><p><strong><a class="reference external" href="https://github.com/karpathy/micrograd">Micrograd</a></strong> by Andrej Karpathy<br />
|
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<em>Minimal autograd engine in 100 lines. <strong>Micrograd shows you the math, TinyTorch shows you the systems.</strong></em></p></li>
|
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<li><p><strong><a class="reference external" href="https://github.com/Kipre/microtorch">Microtorch</a></strong> by Kipre<br />
|
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<em>PyTorch-like API in pure Python. <strong>Microtorch focuses on clean API design, TinyTorch emphasizes systems engineering and scalability.</strong></em></p></li>
|
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<li><p><strong><a class="reference external" href="https://github.com/geohot/tinygrad">Tinygrad</a></strong> by George Hotz<br />
|
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<em>Performance-focused educational framework. <strong>Tinygrad optimizes for speed, TinyTorch optimizes for learning.</strong></em></p></li>
|
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<li><p><strong><a class="reference external" href="https://nnfs.io/">Neural Networks from Scratch</a></strong> by Harrison Kinsley<br />
|
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<em>Math-heavy implementation approach. <strong>NNFS focuses on algorithms, TinyTorch focuses on systems engineering.</strong></em></p></li>
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</ul>
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</section>
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</section>
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<hr class="docutils" />
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<section id="production-internals">
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<h2>Production Internals<a class="headerlink" href="#production-internals" title="Permalink to this heading">#</a></h2>
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<section id="framework-deep-dives">
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<h3>Framework Deep Dives<a class="headerlink" href="#framework-deep-dives" title="Permalink to this heading">#</a></h3>
|
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<ul class="simple">
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<li><p><strong><a class="reference external" href="http://blog.ezyang.com/2019/05/pytorch-internals/">PyTorch Internals</a></strong> by Edward Yang<br />
|
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<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>
|
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<li><p><strong><a class="reference external" href="https://pytorch.org/docs/stable/notes/extending.md">PyTorch Documentation: Extending PyTorch</a></strong><br />
|
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<em>Custom operators and autograd functions - apply your TinyTorch knowledge</em></p></li>
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</ul>
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<hr class="docutils" />
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<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>
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</section>
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</section>
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<section id="ready-to-begin-your-journey">
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<h2>Ready to Begin Your Journey?<a class="headerlink" href="#ready-to-begin-your-journey" title="Permalink to this heading">#</a></h2>
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<p><strong>Start with the fundamentals and build your way up.</strong></p>
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<p>See <a class="reference internal" href="tito-essentials.html"><span class="doc std std-doc">Essential Commands</span></a> for complete TITO command reference.</p>
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<p><strong>Your Next Steps:</strong></p>
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<ol class="arabic simple">
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<li><p>See <a class="reference internal" href="quickstart-guide.html"><span class="doc std std-doc">Quick Start Guide</span></a> for 15-minute hands-on experience</p></li>
|
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<li><p>See <a class="reference internal" href="student-workflow.html"><span class="doc std std-doc">Student Workflow</span></a> for understanding the development workflow</p></li>
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<li><p>See <a class="reference internal" href="chapters/00-introduction.html"><span class="doc std std-doc">Course Introduction</span></a> for deep dive into course philosophy</p></li>
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<h4 style="margin: 0 0 1rem 0; color: #495057;">Transform from Framework User to Systems Engineer</h4>
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<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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