TinyTorch/COMPLETE_MODULE_ROADMAP.md

TinyTorch Complete Module Roadmap

20-Module ML Systems Course with Competition System

PHASE 1: FOUNDATION (Modules 1-6)

Build the core mathematical infrastructure for neural networks.

• Module 01: setup - Development environment configuration
• Module 02: tensor - Core data structures with autodiff support (backward design: built-in grad support)
• Module 03: activations - ReLU, Sigmoid, nonlinearity functions
• Module 04: layers - Dense layers, network building blocks
• Module 05: losses - MSE, CrossEntropy, BCE loss functions
• Module 06: autograd - Automatic differentiation engine

Capability Unlocked: Networks can learn through backpropagation Historical Example: XOR Problem (1969) - Solve what stumped AI for a decade

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PHASE 2: TRAINING SYSTEMS (Modules 7-10)

Build complete training pipelines for real datasets.

• Module 07: dataloader - Data pipelines, batching, real datasets (moved from 09)
• Module 08: optimizers - SGD, Adam optimization algorithms
• Module 09: spatial - Conv2D, pooling for image processing (moved from 07)
• Module 10: training - Complete training loops with validation

Capability Unlocked: Train deep networks on real datasets Historical Examples:

• After Module 9: LeNet (1998) - First CNN for digit recognition
• After Module 10: AlexNet (2012) - Deep learning revolution

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PHASE 3: LANGUAGE MODELS (Modules 11-14)

Build modern transformer architectures for NLP.

• Module 11: tokenization - Text preprocessing and tokenization
• Module 12: embeddings - Word vectors, positional encoding
• Module 13: attention - Self-attention mechanisms
• Module 14: transformers - Complete transformer architecture

Capability Unlocked: Build GPT-style language models Historical Example: GPT (2018) - Foundation of modern AI

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PHASE 4: SYSTEM OPTIMIZATION (Modules 15-19)

Transform educational code into production-ready systems through progressive optimization.

• Module 15: acceleration - Core performance optimization

  • Journey from educational loops to optimized operations
  • Cache-friendly blocking for matrix multiplication
  • NumPy vectorization (10-100x speedups)
  • Transparent backend dispatch (existing code runs faster automatically!)

• Module 16: caching - Memory optimization patterns

  • KV caching for transformer inference
  • Incremental computation techniques
  • Autoregressive generation optimization
  • Memory vs computation tradeoffs

• Module 17: precision - Numerical optimization

  • Post-training INT8 quantization
  • Calibration and scaling techniques
  • Accuracy vs performance tradeoffs
  • Memory footprint reduction

• Module 18: compression - Model size optimization

  • Magnitude-based pruning
  • Structured vs unstructured sparsity
  • Knowledge distillation basics
  • Deployment optimization

• Module 19: benchmarking - Performance analysis

  • Profiling and bottleneck identification
  • Memory usage analysis
  • Comparative benchmarking
  • Scientific performance measurement

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PHASE 5: CAPSTONE PROJECT (Module 20)

• Module 20: capstone - Complete ML system
  • Combine all optimization techniques
  • Build optimized end-to-end systems
  • Example projects:
    • Optimized CIFAR-10 trainer (75% accuracy, minimal resources)
    • Efficient GPT inference engine (memory-constrained)
    • Custom optimization challenge
  • Deploy production-ready ML systems

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Key Design Principles

1. Backward Design Philosophy

Each module is designed with future needs in mind:

• Tensors (Module 2): Built with gradient support from day 1
• Layers (Module 4): Parameter management ready for optimizers
• Training (Module 10): Memory tracking for optimization modules
• Transformers (Module 14): KV structure ready for caching

2. Backend Dispatch Architecture

# Students run SAME code throughout
model.train()  # Uses appropriate backend automatically

# Module 1-14: Naive backend (for learning)
# Module 15+: Optimized backend (for performance)
# Zero code changes needed!

3. Progressive Optimization Journey

• Understanding through implementation (Modules 1-14): Build with loops for clarity
• Systematic optimization (Modules 15-19): Transform loops into production code
• Transparent acceleration: Optimizations work automatically on existing code
• Real-world techniques: Learn optimizations used in PyTorch/TensorFlow

4. Historical Context

Examples map to ML breakthroughs:

• 1957: Perceptron (Module 4)
• 1969: XOR Solution (Module 6)
• 1998: LeNet (Module 9)
• 2012: AlexNet (Module 10)
• 2018: GPT (Module 14)

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Learning Progression

Weeks 1-6: Foundation

Students build mathematical infrastructure and understand how neural networks work.

Weeks 7-10: Training Systems

Students build complete training pipelines and understand how to scale to real datasets.

Weeks 11-14: Modern AI

Students build transformer architectures that power ChatGPT and modern AI.

Weeks 15-19: System Optimization

Students transform educational code into production-ready systems through progressive optimization techniques.

Week 20: Capstone Project

Students combine all techniques to build complete, optimized ML systems from scratch.

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Success Metrics

By completion, students will have:

• ✅ Built every component of modern ML systems from scratch
• ✅ Recreated the major breakthroughs in AI history
• ✅ Transformed educational loops into production-ready code (10-100x speedups)
• ✅ Understood why PyTorch, TensorFlow are designed the way they are
• ✅ Mastered real-world optimization techniques (caching, quantization, pruning)
• ✅ Built complete ML systems that transparently optimize themselves

Ultimate Goal: Students who can read PyTorch source code and think "I understand why they did it this way - I built this myself in TinyTorch!"