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<h1>Learning Resources</h1>
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<h2> Contents </h2>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#companion-textbook">Companion Textbook</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-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#minimal-frameworks">Minimal Frameworks</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#production-framework-internals">Production Framework Internals</a></li>
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<section id="learning-resources">
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<h1>Learning Resources<a class="headerlink" href="#learning-resources" title="Link to this heading">#</a></h1>
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<p><strong>TinyTorch teaches you to <em>build</em> ML systems. These resources help you understand the <em>why</em> behind what you’re building.</strong></p>
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<hr class="docutils" />
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<section id="companion-textbook">
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<h2>Companion Textbook<a class="headerlink" href="#companion-textbook" title="Link 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="Link to this heading">#</a></h3>
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<p><strong><a class="reference external" href="https://mlsysbook.ai">mlsysbook.ai</a></strong> by Prof. Vijay Janapa Reddi (Harvard University)</p>
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<div style="background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb 100%); border-left: 5px solid #1976d2; padding: 1.5rem; border-radius: 0.5rem; margin: 1.5rem 0;">
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<p style="margin: 0; color: #0d47a1; font-size: 1.05rem; line-height: 1.6;">
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<strong>TinyTorch began as hands-on labs for this textbook.</strong> While TinyTorch can be used standalone, the ML Systems book provides the theoretical depth and production context behind every module you build.
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</p>
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</div>
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<p><strong>What it teaches</strong>: Systems engineering for production ML—memory hierarchies, performance optimization, deployment strategies, and the engineering decisions behind modern ML frameworks.</p>
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<p><strong>How it connects to TinyTorch</strong>:</p>
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<ul class="simple">
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<li><p>TinyTorch modules directly implement concepts from the book’s chapters</p></li>
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<li><p>The book explains <em>why</em> PyTorch, TensorFlow, and JAX make certain design decisions</p></li>
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<li><p>Together, they provide both hands-on implementation and theoretical understanding</p></li>
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</ul>
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<p><strong>When to use it</strong>: Read in parallel with TinyTorch. When you implement Module 05 (Autograd), read the book’s chapter on automatic differentiation to understand the systems engineering behind your code.</p>
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</section>
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</section>
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<hr class="docutils" />
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<section id="related-academic-courses">
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<h2>Related Academic Courses<a class="headerlink" href="#related-academic-courses" title="Link to this heading">#</a></h2>
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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)
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<em>Production ML systems and deployment</em></p></li>
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<li><p><strong><a class="reference external" href="https://efficientml.ai">TinyML and Efficient Deep Learning</a></strong> (MIT 6.5940)
|
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<em>Edge computing, model compression, and efficient ML</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)
|
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<em>TinyML systems and resource-constrained ML</em></p></li>
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<li><p><strong><a class="reference external" href="http://cs231n.stanford.edu/">CS 231n: Convolutional Neural Networks</a></strong> (Stanford)
|
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<em>Computer vision - complements TinyTorch Modules 08-09</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)
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<em>Transformers and NLP - complements TinyTorch Modules 10-13</em></p></li>
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</ul>
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</section>
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<hr class="docutils" />
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<section id="other-textbooks">
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<h2>Other Textbooks<a class="headerlink" href="#other-textbooks" title="Link to this heading">#</a></h2>
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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 Goodfellow, Bengio, Courville
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<em>Mathematical foundations 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
|
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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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<hr class="docutils" />
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<section id="minimal-frameworks">
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<h2>Minimal Frameworks<a class="headerlink" href="#minimal-frameworks" title="Link to this heading">#</a></h2>
|
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<p><strong>Alternative approaches to building ML from scratch:</strong></p>
|
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<ul class="simple">
|
||
<li><p><strong><a class="reference external" href="https://github.com/karpathy/micrograd">micrograd</a></strong> by Andrej Karpathy
|
||
<em>Autograd in 100 lines. Perfect 2-hour intro before TinyTorch.</em></p></li>
|
||
<li><p><strong><a class="reference external" href="https://github.com/karpathy/nanoGPT">nanoGPT</a></strong> by Andrej Karpathy
|
||
<em>Minimalist GPT implementation. Complements TinyTorch Modules 12-13.</em></p></li>
|
||
<li><p><strong><a class="reference external" href="https://github.com/geohot/tinygrad">tinygrad</a></strong> by George Hotz
|
||
<em>Performance-focused educational framework with GPU acceleration.</em></p></li>
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</ul>
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</section>
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<hr class="docutils" />
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<section id="production-framework-internals">
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<h2>Production Framework Internals<a class="headerlink" href="#production-framework-internals" title="Link to this heading">#</a></h2>
|
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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
|
||
<em>How PyTorch actually works under the hood</em></p></li>
|
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<li><p><strong><a class="reference external" href="https://pytorch.org/docs/stable/notes/extending.md">PyTorch: Extending PyTorch</a></strong>
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<em>Custom operators and autograd functions</em></p></li>
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</ul>
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<hr class="docutils" />
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<p><strong>Ready to start?</strong> See the <strong><a class="reference internal" href="#quickstart-guide"><span class="xref myst">Quick Start Guide</span></a></strong> for a 15-minute hands-on introduction.</p>
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