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78 lines
3.6 KiB
Markdown
# 📚 Additional Learning Resources
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**Complement your TinyTorch journey with these carefully selected resources.**
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While TinyTorch teaches you to build complete ML systems from scratch, these resources provide broader context, alternative perspectives, and production tools.
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---
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## 🎓 Academic Courses
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### Machine Learning Systems
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- **[CS 329S: Machine Learning Systems Design](https://stanford-cs329s.github.io/)** (Stanford)
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*Production ML systems, infrastructure, and deployment at scale*
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- **[CS 6.S965: TinyML and Efficient Deep Learning](https://hanlab.mit.edu/courses/2024-fall-65940)** (MIT)
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*Edge computing, model compression, and efficient ML algorithms*
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- **[CS 249r: Tiny Machine Learning](https://sites.google.com/g.harvard.edu/tinyml/home)** (Harvard)
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*TinyML systems, edge AI, and resource-constrained machine learning*
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### Deep Learning Foundations
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- **[CS 231n: Convolutional Neural Networks](http://cs231n.stanford.edu/)** (Stanford)
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*Computer vision and CNN architectures - complements TinyTorch spatial modules*
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- **[CS 224n: Natural Language Processing](http://web.stanford.edu/class/cs224n/)** (Stanford)
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*NLP and transformers - perfect follow-up to TinyTorch attention module*
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---
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## 📖 Recommended Books
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### Systems & Engineering
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- **[Machine Learning Systems](https://mlsysbook.ai)** by Prof. Vijay Janapa Reddi (Harvard)
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*Comprehensive systems perspective on ML engineering and optimization - the perfect companion to TinyTorch*
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- **[Designing Machine Learning Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/)** by Chip Huyen
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*Production ML engineering, data pipelines, and system design*
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- **[Machine Learning Engineering](https://www.mlebook.com/wiki/doku.php)** by Andriy Burkov
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*End-to-end ML project lifecycle and best practices*
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### Implementation & Theory
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- **[Deep Learning](https://www.deeplearningbook.org/)** by Ian Goodfellow, Yoshua Bengio, Aaron Courville
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*Mathematical foundations - the theory behind what you implement in TinyTorch*
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- **[Hands-On Machine Learning](https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/)** by Aurélien Géron
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*Practical implementations using established frameworks*
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---
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## 🛠️ Alternative Implementations
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**Different approaches to building ML systems from scratch - see how others tackle the same challenge:**
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### Minimal Frameworks
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- **[Micrograd](https://github.com/karpathy/micrograd)** by Andrej Karpathy
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*Minimal autograd engine in 100 lines. **Micrograd shows you the math, TinyTorch shows you the systems.***
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- **[Tinygrad](https://github.com/geohot/tinygrad)** by George Hotz
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*Performance-focused educational framework. **Tinygrad optimizes for speed, TinyTorch optimizes for learning.***
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- **[Neural Networks from Scratch](https://nnfs.io/)** by Harrison Kinsley
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*Math-heavy implementation approach. **NNFS focuses on algorithms, TinyTorch focuses on systems engineering.***
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---
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## 🏭 Production Internals
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### Framework Deep Dives
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- **[PyTorch Internals](http://blog.ezyang.com/2019/05/pytorch-internals/)** by Edward Yang
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*How PyTorch actually works under the hood - a great read as see what you built in TinyTorch corresponds to the real PyTorch*
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- **[PyTorch Documentation: Extending PyTorch](https://pytorch.org/docs/stable/notes/extending.html)**
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*Custom operators and autograd functions - apply your TinyTorch knowledge*
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---
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*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.* 🚀 |