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Acknowledgments

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Acknowledgments

TinyTorch stands on the shoulders of giants.

This project draws inspiration from pioneering educational ML frameworks and owes its existence to the open source community's commitment to accessible ML education.

Core Inspirations

MiniTorch

minitorch.github.io by Sasha Rush (Cornell Tech)

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.

What MiniTorch teaches: Automatic differentiation through minimal, elegant implementations

How TinyTorch differs: Extends to full systems engineering including optimization, profiling, and production deployment across Foundation → Architecture → Optimization tiers

When to use MiniTorch: Excellent complement for deep mathematical understanding of autodifferentiation

Connection to TinyTorch: Modules 06-08 (Autograd, Optimizers, Training) share philosophical DNA with MiniTorch's core pedagogy

micrograd

github.com/karpathy/micrograd by Andrej Karpathy

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.

What micrograd teaches: Autograd engine in 100 beautiful lines of Python

How TinyTorch differs: Comprehensive framework covering vision, language, and production systems (20 modules vs. single-file implementation)

When to use micrograd: Perfect 2-hour introduction before starting TinyTorch

Connection to TinyTorch: Module 06 (Autograd) teaches the same core concepts with systems engineering focus

nanoGPT

github.com/karpathy/nanoGPT by Andrej Karpathy

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.

What nanoGPT teaches: Clean transformer implementation for understanding GPT architecture

How TinyTorch differs: Build transformers from tensors up, understanding all dependencies from scratch

When to use nanoGPT: Complement to TinyTorch Modules 10-13 for transformer-specific deep-dive

Connection to TinyTorch: Module 13 (Transformers) culminates in similar architecture built from your own tensor operations

tinygrad

github.com/geohot/tinygrad by George Hotz

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.

What tinygrad teaches: Performance-focused educational framework with actual GPU acceleration

How TinyTorch differs: Pedagogy-first with explicit systems thinking and scaffolding (educational over performant)

When to use tinygrad: After TinyTorch for performance optimization deep-dive and GPU programming

Connection to TinyTorch: Modules 14-19 (Optimization Tier) share production systems focus

What Makes TinyTorch Unique

TinyTorch combines inspiration from these projects into a comprehensive ML systems course:

  • Comprehensive Scope: Only educational framework covering Foundation → Architecture → Optimization
  • Systems Thinking: Every module includes profiling, complexity analysis, production context
  • Historical Validation: Milestone system proving implementations through ML history (1958 → 2018)
  • Pedagogical Scaffolding: Progressive disclosure, Build → Use → Reflect methodology
  • Production Context: Direct connections to PyTorch, TensorFlow, and industry practices

ML Systems Book Contributors

TinyTorch is part of the broader ML Systems Book ecosystem. These contributors have helped build the educational foundation that TinyTorch extends.

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<div class="contributor-grid">
  <a href="https://github.com/hzeljko" class="contributor" title="Zeljko Hrcek">
    
    <span class="name">Zeljko Hrcek</span>
  </a>
  <a href="https://github.com/Mjrovai" class="contributor" title="Marcelo Rovai">
    
    <span class="name">Marcelo Rovai</span>
  </a>
  <a href="https://github.com/jasonjabbour" class="contributor" title="Jason Jabbour">
    
    <span class="name">Jason Jabbour</span>
  </a>
  <a href="https://github.com/uchendui" class="contributor" title="Ikechukwu Uchendu">
    
    <span class="name">Ike Uchendu</span>
  </a>
  <a href="https://github.com/Naeemkh" class="contributor" title="Naeem Khoshnevis">
    
    <span class="name">Naeem K.</span>
  </a>
  <a href="https://github.com/Sara-Khosravi" class="contributor" title="Sara Khosravi">
    
    <span class="name">Sara Khosravi</span>
  </a>
  <a href="https://github.com/didier-durand" class="contributor" title="Didier Durand">
    
    <span class="name">Didier Durand</span>
  </a>
  <a href="https://github.com/18jeffreyma" class="contributor" title="Jeffrey Ma">
    
    <span class="name">Jeffrey Ma</span>
  </a>
  <a href="https://github.com/V0XNIHILI" class="contributor" title="Douwe den Blanken">
    
    <span class="name">Douwe dB</span>
  </a>
  <a href="https://github.com/shanzehbatool" class="contributor" title="Shanzeh Batool">
    
    <span class="name">Shanzeh B.</span>
  </a>
  <a href="https://github.com/eliasab16" class="contributor" title="Elias">
    
    <span class="name">Elias</span>
  </a>
  <a href="https://github.com/JaredP94" class="contributor" title="Jared Ping">
    
    <span class="name">Jared Ping</span>
  </a>
  <a href="https://github.com/ishapira1" class="contributor" title="Itai Shapira">
    
    <span class="name">Itai Shapira</span>
  </a>
  <a href="https://github.com/jaysonzlin" class="contributor" title="Jayson Lin">
    
    <span class="name">Jayson Lin</span>
  </a>
  <a href="https://github.com/sophiacho1" class="contributor" title="Sophia Cho">
    
    <span class="name">Sophia Cho</span>
  </a>
  <a href="https://github.com/alxrod" class="contributor" title="Alex Rodriguez">
    
    <span class="name">Alex Rodriguez</span>
  </a>
  <a href="https://github.com/korneelf1" class="contributor" title="Korneel Van den Berghe">
    
    <span class="name">Korneel VdB</span>
  </a>
  <a href="https://github.com/colbybanbury" class="contributor" title="Colby Banbury">
    
    <span class="name">Colby Banbury</span>
  </a>
  <a href="https://github.com/zishenwan" class="contributor" title="Zishen Wan">
    
    <span class="name">Zishen Wan</span>
  </a>
</div>

View all 40+ contributors on GitHub →

License

TinyTorch is released under the MIT License, ensuring it remains free and open for educational use.

Thank you to everyone building the future of accessible ML education.