TinyTorch/site/learning-progress.md

# 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 20 modules and 6 historical milestones</p>
</div>

**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.md) for the complete development cycle, best practices, and troubleshooting.

## Understanding Modules vs Checkpoints vs Milestones

<div style="background: #e3f2fd; padding: 1.5rem; border-radius: 0.5rem; border-left: 4px solid #2196f3; margin: 1.5rem 0;">

**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 status` to view
- Tracks capability mastery
- Not required for the core workflow

See [Journey Through ML History](chapters/milestones.md) for milestone details.

</div>

## 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.md) 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](quickstart-guide.md)** 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.md)** 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:

<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>
</div>

## 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.md)** 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.