# 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](student-workflow.html)** 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 MLPerf)
**Checkpoints (21 total)**: Optional progress tracking
- Use `tito checkpoint status` to view
- Tracks capability mastery
- Not required for the core workflow
**📖 See [Journey Through ML History](chapters/milestones.html)** 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](chapters/00-introduction.html#three-tier-learning-pathway-build-complete-ml-systems)** 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 - MLPerf-style comparison
- [ ] **Module 20**: Competition - Capstone challenge
**📖 See [Quick Start Guide](quickstart-guide.html)** for immediate hands-on experience with your first module.
## Optional: Checkpoint System
Track capability mastery with the optional checkpoint system:
```bash
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](tito-essentials.html)** 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:
## How to Track Your Progress
The essential workflow:
```bash
# 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](student-workflow.html)** 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.