# Track Your Progress
Monitor Your Learning Journey
Track your capability development through 16 essential ML systems skills
**Purpose**: Monitor your capability development through the 21-checkpoint system. Track progress from foundation skills to production ML systems mastery.
Track your progression through 21 essential ML systems capabilities. Each checkpoint represents fundamental competencies you'll master through hands-on implementation—from tensor operations to production-ready systems.
## How to Track Your Progress
🎯 Capability-Based Learning
Use TinyTorch's 21-checkpoint system to monitor your capability development. Track progress from foundation skills to production ML systems mastery.
**📖 See [Essential Commands](tito-essentials.html)** for complete progress tracking commands and workflow.
## Your Learning Path Overview
TinyTorch organizes learning through **three pedagogically-motivated tiers**, each building essential ML systems capabilities:
**📖 See [Three-Tier Learning Structure](chapters/00-introduction.html#three-tier-learning-pathway-build-complete-ml-systems)** for detailed tier breakdown, time estimates, and learning outcomes.
## Student Learning Journey
### Typical Student Progression by Tier
- **🏗️ Foundation Tier (6-8 weeks)**: Build mathematical infrastructure - tensors, autograd, optimizers, training loops
- **🧠 Intelligence Tier (4-6 weeks)**: Implement modern AI architectures - CNNs for vision, transformers for language
- **⚡ Optimization Tier (4-6 weeks)**: Deploy production systems - profiling, quantization, acceleration
### Study Approaches
- **Complete Builder** (14-18 weeks): Implement all three tiers from scratch
- **Focused Explorer** (4-8 weeks): Pick specific tiers based on your goals
- **Guided Learner** (8-12 weeks): Study implementations with hands-on exercises
**📖 See [Quick Start Guide](quickstart-guide.html)** for immediate hands-on experience with your first module.
## 21 Core Capabilities
Track progress through essential ML systems competencies:
```{admonition} Capability Tracking
:class: note
Each checkpoint validates mastery of fundamental ML systems skills.
```
| Checkpoint | Capability Question | Modules Required | Status |
|------------|-------------------|------------------|--------|
| 00 | Can I set up my environment? | 01 | ⬜ Setup |
| 01 | Can I manipulate tensors? | 02 | ⬜ Foundation |
| 02 | Can I add nonlinearity? | 03 | ⬜ Intelligence |
| 03 | Can I build network layers? | 04 | ⬜ Components |
| 04 | Can I measure loss? | 05 | ⬜ Networks |
| 05 | Can I compute gradients? | 06 | ⬜ Learning |
| 06 | Can I optimize parameters? | 07 | ⬜ Optimization |
| 07 | Can I train models? | 08 | ⬜ Training |
| 08 | Can I process images? | 09 | ⬜ Vision |
| 09 | Can I load data efficiently? | 10 | ⬜ Data |
| 10 | Can I process text? | 11 | ⬜ Language |
| 11 | Can I create embeddings? | 12 | ⬜ Representation |
| 12 | Can I implement attention? | 13 | ⬜ Attention |
| 13 | Can I build transformers? | 14 | ⬜ Architecture |
| 14 | Can I profile performance? | 14 | ⬜ Deployment |
| 15 | Can I accelerate algorithms? | 15 | ⬜ Acceleration |
| 16 | Can I quantize models? | 16 | ⬜ Quantization |
| 17 | Can I compress networks? | 17 | ⬜ Compression |
| 18 | Can I cache computations? | 18 | ⬜ Caching |
| 19 | Can I benchmark competitively? | 19 | ⬜ Competition |
| 20 | Can I build complete language models? | 20 | ⬜ TinyGPT Capstone |
**📖 See [Essential Commands](tito-essentials.html)** for progress monitoring 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:
## Track Your Progress
To monitor your capability development and learning progression, use the TITO checkpoint commands.
**📖 See [Essential Commands](tito-essentials.html)** for complete command reference and usage examples.
**Approach**: You're building ML systems engineering capabilities through hands-on implementation. Each capability checkpoint validates practical competency, not just theoretical understanding.