📚 Additional Learning Resources

Complement Your TinyTorch Journey

Carefully selected resources for broader context, alternative perspectives, and production tools

While TinyTorch teaches you to build complete ML systems from scratch, these resources provide broader context, alternative perspectives, and production tools.

TinyTorch Learning Resources:

• Track Your Progress → - Monitor capability development and learning progression

• Progress Tracking → - Technical details on capability testing

• Testing Guide → - Comprehensive testing methodology

• Achievement Showcase → - Portfolio development and career readiness

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🎓 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

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📖 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

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🛠️ 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.

• Microtorch by Kipre
  PyTorch-like API in pure Python. Microtorch focuses on clean API design, TinyTorch emphasizes systems engineering and scalability.

• 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.

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🏭 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

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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.

🚀 Ready to Begin Your Journey?

Start with the fundamentals and build your way up.

📖 See Essential Commands for complete TITO command reference.

Your Next Steps:

1. Quick Start Guide → - 15-minute hands-on experience

2. Track Your Progress → - Understand capability development

3. Course Introduction → - Deep dive into course philosophy

🎯 Transform from Framework User to Systems Engineer

These external resources complement the hands-on systems building you'll do in TinyTorch