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<h1>Credits & Acknowledgments</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="#core-inspirations">Core Inspirations</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="#minitorch">MiniTorch</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#micrograd">micrograd</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#nanogpt">nanoGPT</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#tinygrad">tinygrad</a></li>
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</ul>
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</li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#what-makes-tinytorch-unique">What Makes TinyTorch Unique</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#community-contributors">Community Contributors</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-to-contribute">How to Contribute</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#license">License</a></li>
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<article class="bd-article">
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<section id="credits-acknowledgments">
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<h1>Credits & Acknowledgments<a class="headerlink" href="#credits-acknowledgments" title="Link to this heading">#</a></h1>
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<p><strong>TinyTorch stands on the shoulders of giants.</strong></p>
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<p>This project draws inspiration from pioneering educational ML frameworks and owes its existence to the open source community’s commitment to accessible ML education.</p>
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<hr class="docutils" />
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<section id="core-inspirations">
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<h2>Core Inspirations<a class="headerlink" href="#core-inspirations" title="Link to this heading">#</a></h2>
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<section id="minitorch">
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<h3>MiniTorch<a class="headerlink" href="#minitorch" title="Link to this heading">#</a></h3>
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<p><strong><a class="reference external" href="https://minitorch.github.io/">minitorch.github.io</a></strong> by Sasha Rush (Cornell Tech)</p>
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<p>TinyTorch’s pedagogical DNA comes from MiniTorch’s brilliant “build a framework from scratch” approach. MiniTorch pioneered teaching ML through implementation rather than usage, proving students gain deeper understanding by building systems themselves.</p>
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<p><strong>What MiniTorch teaches</strong>: Automatic differentiation through minimal, elegant implementations</p>
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<p><strong>How TinyTorch differs</strong>: Extends to full systems engineering including optimization, profiling, and production deployment across Foundation → Architecture → Optimization tiers</p>
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<p><strong>When to use MiniTorch</strong>: Excellent complement for deep mathematical understanding of autodifferentiation</p>
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<p><strong>Connection to TinyTorch</strong>: Modules 05-07 (Autograd, Optimizers, Training) share philosophical DNA with MiniTorch’s core pedagogy</p>
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</section>
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<hr class="docutils" />
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<section id="micrograd">
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<h3>micrograd<a class="headerlink" href="#micrograd" title="Link to this heading">#</a></h3>
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<p><strong><a class="reference external" href="https://github.com/karpathy/micrograd">github.com/karpathy/micrograd</a></strong> by Andrej Karpathy</p>
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<p>Micrograd demonstrated that automatic differentiation—the heart of modern ML—can be taught in ~100 lines of elegant Python. Its clarity and simplicity inspired TinyTorch’s emphasis on understandable implementations.</p>
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<p><strong>What micrograd teaches</strong>: Autograd engine in 100 beautiful lines of Python</p>
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<p><strong>How TinyTorch differs</strong>: Comprehensive framework covering vision, language, and production systems (20 modules vs. single-file implementation)</p>
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<p><strong>When to use micrograd</strong>: Perfect 2-hour introduction before starting TinyTorch</p>
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<p><strong>Connection to TinyTorch</strong>: Module 05 (Autograd) teaches the same core concepts with systems engineering focus</p>
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</section>
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<hr class="docutils" />
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<section id="nanogpt">
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<h3>nanoGPT<a class="headerlink" href="#nanogpt" title="Link to this heading">#</a></h3>
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<p><strong><a class="reference external" href="https://github.com/karpathy/nanoGPT">github.com/karpathy/nanoGPT</a></strong> by Andrej Karpathy</p>
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<p>nanoGPT’s minimalist transformer implementation showed how to teach modern architectures without framework abstraction. TinyTorch’s transformer modules (12, 13) follow this philosophy: clear, hackable implementations that reveal underlying mathematics.</p>
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<p><strong>What nanoGPT teaches</strong>: Clean transformer implementation for understanding GPT architecture</p>
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<p><strong>How TinyTorch differs</strong>: Build transformers from tensors up, understanding all dependencies from scratch</p>
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<p><strong>When to use nanoGPT</strong>: Complement to TinyTorch Modules 10-13 for transformer-specific deep-dive</p>
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<p><strong>Connection to TinyTorch</strong>: Module 13 (Transformers) culminates in similar architecture built from your own tensor operations</p>
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</section>
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<hr class="docutils" />
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<section id="tinygrad">
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<h3>tinygrad<a class="headerlink" href="#tinygrad" title="Link to this heading">#</a></h3>
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<p><strong><a class="reference external" href="https://github.com/geohot/tinygrad">github.com/geohot/tinygrad</a></strong> by George Hotz</p>
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<p>Tinygrad proves educational frameworks can achieve impressive performance. While TinyTorch optimizes for learning clarity over speed, tinygrad’s emphasis on efficiency inspired our Optimization Tier’s production-focused modules.</p>
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<p><strong>What tinygrad teaches</strong>: Performance-focused educational framework with actual GPU acceleration</p>
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<p><strong>How TinyTorch differs</strong>: Pedagogy-first with explicit systems thinking and scaffolding (educational over performant)</p>
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<p><strong>When to use tinygrad</strong>: After TinyTorch for performance optimization deep-dive and GPU programming</p>
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<p><strong>Connection to TinyTorch</strong>: Modules 14-19 (Optimization Tier) share production systems focus</p>
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</section>
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</section>
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<hr class="docutils" />
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<section id="what-makes-tinytorch-unique">
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<h2>What Makes TinyTorch Unique<a class="headerlink" href="#what-makes-tinytorch-unique" title="Link to this heading">#</a></h2>
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<p>TinyTorch combines inspiration from these projects into a comprehensive ML systems course:</p>
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<ul class="simple">
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<li><p><strong>Comprehensive Scope</strong>: Only educational framework covering Foundation → Architecture → Optimization</p></li>
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<li><p><strong>Systems Thinking</strong>: Every module includes profiling, complexity analysis, production context</p></li>
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<li><p><strong>Historical Validation</strong>: Milestone system proving implementations through ML history (1957 → 2018)</p></li>
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<li><p><strong>Pedagogical Scaffolding</strong>: Progressive disclosure, Build → Use → Reflect methodology</p></li>
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<li><p><strong>Production Context</strong>: Direct connections to PyTorch, TensorFlow, and industry practices</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="community-contributors">
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<h2>Community Contributors<a class="headerlink" href="#community-contributors" title="Link to this heading">#</a></h2>
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<p>TinyTorch is built by students, educators, and ML engineers who believe in accessible systems education.</p>
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<p><strong><a class="reference external" href="https://github.com/harvard-edge/TinyTorch/graphs/contributors">View all contributors on GitHub</a></strong></p>
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<section id="how-to-contribute">
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<h2>How to Contribute<a class="headerlink" href="#how-to-contribute" title="Link to this heading">#</a></h2>
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<p>TinyTorch is open source and welcomes contributions:</p>
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<ul class="simple">
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<li><p><strong>Found a bug?</strong> Report it on <a class="reference external" href="https://github.com/harvard-edge/TinyTorch/issues">GitHub Issues</a></p></li>
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<li><p><strong>Improved documentation?</strong> Submit a pull request</p></li>
|
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<li><p><strong>Built something cool?</strong> Share it in <a class="reference external" href="https://github.com/harvard-edge/TinyTorch/discussions">GitHub Discussions</a></p></li>
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</ul>
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<p><strong><a class="reference external" href="https://github.com/harvard-edge/TinyTorch/blob/main/CONTRIBUTING.md">See contribution guidelines</a></strong></p>
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<h2>License<a class="headerlink" href="#license" title="Link to this heading">#</a></h2>
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<p>TinyTorch is released under the MIT License, ensuring it remains free and open for educational use.</p>
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<p><strong>Thank you to everyone building the future of accessible ML education.</strong></p>
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