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Track Your Progress
Monitor Your Learning Journey
Track your capability development through 20 modules and 6 historical milestones
Purpose: Monitor your progress as you build a complete ML framework from scratch. Track module completion and milestone achievements.
The Core Workflow
TinyTorch follows a simple three-step cycle: Edit modules → Export to package → Validate with milestones
See Student Workflow for the complete development cycle, best practices, and troubleshooting.
Understanding Modules vs Checkpoints vs Milestones
Modules (18 total): What you're building - the actual code implementations
- Located in
modules/source/ - You implement each component from scratch
- Export with
tito module complete N
Milestones (6 total): How you validate - historical proof scripts
- Located in
milestones/ - Run scripts that use YOUR implementations
- Recreate ML history (1957 Perceptron → 2018 Torch Olympics)
Checkpoints (21 total): Optional progress tracking
- Use
tito checkpoint statusto view - Tracks capability mastery
- Not required for the core workflow
See Journey Through ML History for milestone details.
Your Learning Path Overview
TinyTorch organizes 20 modules through three pedagogically-motivated tiers: Foundation (build mathematical infrastructure), Architecture (implement modern AI), and Optimization (deploy production systems).
See Three-Tier Learning Structure for complete tier breakdown, detailed module descriptions, time estimates, and learning outcomes.
Module Progression Checklist
Track your journey through the 20 modules:
- Module 01: Tensor - N-dimensional arrays
- Module 02: Activations - ReLU, Softmax
- Module 03: Layers - Linear layers
- Module 04: Losses - CrossEntropyLoss, MSELoss
- Module 05: Autograd - Automatic differentiation
- Module 06: Optimizers - SGD, Adam
- Module 07: Training - Complete training loops
- Module 08: DataLoader - Batching and pipelines
- Module 09: Spatial - Conv2d, MaxPool2d
- Module 10: Tokenization - Character-level tokenizers
- Module 11: Embeddings - Token and positional embeddings
- Module 12: Attention - Multi-head self-attention
- Module 13: Transformers - LayerNorm, GPT
- Module 14: Profiling - Performance measurement
- Module 15: Quantization - INT8/FP16
- Module 16: Compression - Pruning techniques
- Module 17: Memoization - KV-cache
- Module 18: Acceleration - Batching strategies
- Module 19: Benchmarking - Torch Olympics-style comparison
- Module 20: Competition - Capstone challenge
📖 See Quick Start Guide for immediate hands-on experience with your first module.
Optional: Checkpoint System
Track capability mastery with the optional checkpoint system:
tito checkpoint status # View your progress
This provides 21 capability checkpoints corresponding to modules and validates your understanding. Helpful for self-assessment but not required for the core workflow.
📖 See Essential Commands for checkpoint commands.
Capability Development Approach
Foundation Building (Checkpoints 0-3)
Capability Focus: Core computational infrastructure
- Environment configuration and dependency management
- Mathematical foundations with tensor operations
- Neural intelligence through nonlinear activation functions
- Network component abstractions and forward propagation
Learning Systems (Checkpoints 4-7)
Capability Focus: Training and optimization
- Loss measurement and error quantification
- Automatic differentiation for gradient computation
- Parameter optimization with advanced algorithms
- Complete training loop implementation
Advanced Architectures (Checkpoints 8-13)
Capability Focus: Specialized neural networks
- Spatial processing for computer vision systems
- Efficient data loading and preprocessing pipelines
- Natural language processing and tokenization
- Representation learning with embeddings
- Attention mechanisms for sequence understanding
- Complete transformer architecture mastery
Production Systems (Checkpoints 14-15)
Capability Focus: Performance and deployment
- Profiling, optimization, and bottleneck analysis
- End-to-end ML systems engineering
- Production-ready deployment and monitoring
Start Building Capabilities
Begin developing ML systems competencies immediately:
Begin Capability Development
Start with foundational capabilities and progress systematically
15-Minute Start → Begin Setup →How to Track Your Progress
The essential workflow:
# 1. Work on a module
cd modules/source/03_layers
jupyter lab 03_layers_dev.py
# 2. Export when ready
tito module complete 03
# 3. Validate with milestones
cd ../../milestones/01_1957_perceptron
python 01_rosenblatt_forward.py # Uses YOUR implementation!
Optional: Use tito checkpoint status to see capability tracking
📖 See Student Workflow for the complete development cycle.
Approach: You're building ML systems engineering capabilities through hands-on implementation. Each module adds new functionality to your framework, and milestones prove it works.