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📚 Additional Learning Resources
Complement your TinyTorch journey with these carefully selected resources.
While TinyTorch teaches you to build complete ML systems from scratch, these resources provide broader context, alternative perspectives, and production tools.
🎓 Academic Courses
Machine Learning Systems
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CS 329S: Machine Learning Systems Design (Stanford)
Production ML systems, infrastructure, and deployment at scale -
CS 6.S965: TinyML and Efficient Deep Learning (MIT)
Edge computing, model compression, and efficient ML algorithms -
CS 249r: Tiny Machine Learning (Harvard)
TinyML systems, edge AI, and resource-constrained machine learning
Deep Learning Foundations
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CS 231n: Convolutional Neural Networks (Stanford)
Computer vision and CNN architectures - complements TinyTorch spatial modules -
CS 224n: Natural Language Processing (Stanford)
NLP and transformers - perfect follow-up to TinyTorch attention module
📖 Recommended Books
Systems & Engineering
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Machine Learning Systems by Prof. Vijay Janapa Reddi (Harvard)
Comprehensive systems perspective on ML engineering and optimization - the perfect companion to TinyTorch -
Designing Machine Learning Systems by Chip Huyen
Production ML engineering, data pipelines, and system design -
Machine Learning Engineering by Andriy Burkov
End-to-end ML project lifecycle and best practices
Implementation & Theory
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Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville
Mathematical foundations - the theory behind what you implement in TinyTorch -
Hands-On Machine Learning by Aurélien Géron
Practical implementations using established frameworks
🛠️ Alternative Implementations
Different approaches to building ML systems from scratch - see how others tackle the same challenge:
Minimal Frameworks
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Micrograd by Andrej Karpathy
Minimal autograd engine in 100 lines. Micrograd shows you the math, TinyTorch shows you the systems. -
Tinygrad by George Hotz
Performance-focused educational framework. Tinygrad optimizes for speed, TinyTorch optimizes for learning. -
Neural Networks from Scratch by Harrison Kinsley
Math-heavy implementation approach. NNFS focuses on algorithms, TinyTorch focuses on systems engineering.
🏭 Production Internals
Framework Deep Dives
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PyTorch Internals by Edward Yang
How PyTorch actually works under the hood - a great read as see what you built in TinyTorch corresponds to the real PyTorch -
PyTorch Documentation: Extending PyTorch
Custom operators and autograd functions - apply your TinyTorch knowledge
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. 🚀