# 📚 Additional Learning Resources — TinyTorch

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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

- 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

- 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

- 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

- 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

- 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

- 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. 🚀