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Getting Started
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<li class="toctree-l1"><a class="reference internal" href="datasets.html">Datasets Guide</a></li>
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<h1>Learning Resources<a class="headerlink" href="#learning-resources" title="Permalink 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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<h2>Companion Textbook<a class="headerlink" href="#companion-textbook" 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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<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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<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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|
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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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<h2>Related Academic Courses<a class="headerlink" href="#related-academic-courses" title="Permalink to this heading">#</a></h2>
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|
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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)
|
||
<em>Production ML systems and deployment</em></p></li>
|
||
<li><p><strong><a class="reference external" href="https://efficientml.ai">TinyML and Efficient Deep Learning</a></strong> (MIT 6.5940)
|
||
<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)
|
||
<em>TinyML systems and resource-constrained ML</em></p></li>
|
||
<li><p><strong><a class="reference external" href="http://cs231n.stanford.edu/">CS 231n: Convolutional Neural Networks</a></strong> (Stanford)
|
||
<em>Computer vision - complements TinyTorch Modules 08-09</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)
|
||
<em>Transformers and NLP - complements TinyTorch Modules 10-13</em></p></li>
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<li><p><strong><a class="reference external" href="https://www.deeplearningbook.org/">Deep Learning</a></strong> by Goodfellow, Bengio, Courville
|
||
<em>Mathematical foundations 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
|
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|
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<h2>Minimal Frameworks<a class="headerlink" href="#minimal-frameworks" title="Permalink to this heading">#</a></h2>
|
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|
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|
||
<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
|
||
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|
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<h2>Production Framework Internals<a class="headerlink" href="#production-framework-internals" title="Permalink 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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<p><strong>Ready to start?</strong> See the <strong><a class="reference internal" href="quickstart-guide.html"><span class="doc std std-doc">Quick Start Guide</span></a></strong> for a 15-minute hands-on introduction.</p>
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