# Track Your Progress

<div style="background: #f8f9fa; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0; text-align: center;">
<h2 style="margin: 0 0 1rem 0; color: #495057;">Monitor Your Learning Journey</h2>
<p style="margin: 0; font-size: 1.1rem; color: #6c757d;">Track your capability development through 16 essential ML systems skills</p>
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**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

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<h4 style="margin: 0 0 1rem 0; color: #1976d2;">🎯 Capability-Based Learning</h4>

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.

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## 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
- **🏛️ Architecture 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:

<div style="background: #f8f9fa; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0; text-align: center;">
<h3 style="margin: 0 0 1rem 0; color: #495057;">Begin Capability Development</h3>
<p style="margin: 0 0 1.5rem 0; color: #6c757d;">Start with foundational capabilities and progress systematically</p>
<a href="quickstart-guide.html" style="display: inline-block; background: #007bff; color: white; padding: 0.75rem 1.5rem; border-radius: 0.25rem; text-decoration: none; font-weight: 500; margin-right: 1rem;">15-Minute Start →</a>
<a href="chapters/01-setup.html" style="display: inline-block; background: #28a745; color: white; padding: 0.75rem 1.5rem; border-radius: 0.25rem; text-decoration: none; font-weight: 500;">Begin Setup →</a>
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## 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.