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9e73f389a5
Updates milestone documentation across all site files to match the actual MILESTONE_SCRIPTS configuration in tito/commands/milestone.py: - Milestone 01 (Perceptron): requires modules 01-03 (not 01-04) - Milestone 02 (XOR Crisis): requires modules 01-03 (not 01-02) - Milestone 05 (Transformers): requires 01-08 + 11-13 (not 01-13) - Milestone 06 (MLPerf): requires 01-08 + 14-19 (not 01-19) Also fixes broken link to chapters/milestones.html (directory does not exist) and corrects path to student notebooks.
104 lines
4.6 KiB
Markdown
104 lines
4.6 KiB
Markdown
# TinyTorch Milestones
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Milestones are capstone experiences that bring together everything you've built in the TinyTorch modules. Each milestone recreates a pivotal moment in ML history using YOUR implementations.
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## How Milestones Work
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After completing a set of modules, you unlock the ability to run a milestone. Each milestone:
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1. **Uses YOUR code** - Every tensor operation, gradient computation, and layer runs on code YOU wrote
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2. **Recreates history** - Experience the same breakthroughs researchers achieved decades ago
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3. **Proves understanding** - If it works, you truly understand how these systems function
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## Available Milestones
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| ID | Name | Year | Required Modules | What You'll Do |
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|----|------|------|------------------|----------------|
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| 01 | Perceptron | 1958 | 01-03 | Build Rosenblatt's first neural network (forward pass) |
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| 02 | XOR Crisis | 1969 | 01-03 | Experience the XOR limitation that triggered AI Winter |
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| 03 | MLP Revival | 1986 | 01-08 | Train MLPs to solve XOR + recognize digits |
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| 04 | CNN Revolution | 1998 | 01-09 | Build LeNet for image recognition |
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| 05 | Transformer Era | 2017 | 01-08, 11-13 | Build attention and generate text |
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| 06 | MLPerf Benchmarks | 2018 | 01-08, 14-19 | Optimize and benchmark your neural networks |
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## Running Milestones
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```bash
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# List available milestones and your progress
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tito milestone list
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# Run a specific milestone (all parts)
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tito milestone run 03
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# Run a specific part of a multi-part milestone
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tito milestone run 03 --part 1 # Part 1: XOR Solved
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tito milestone run 03 --part 2 # Part 2: TinyDigits
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# Get detailed info about a milestone
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tito milestone info 05
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```
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## Directory Structure
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```
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milestones/
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├── 01_1958_perceptron/ # Milestone 01: Rosenblatt's Perceptron
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├── 02_1969_xor/ # Milestone 02: XOR Problem
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├── 03_1986_mlp/ # Milestone 03: Backpropagation MLP
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├── 04_1998_cnn/ # Milestone 04: LeNet CNN
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├── 05_2017_transformer/ # Milestone 05: Attention Mechanism
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├── 06_2018_mlperf/ # Milestone 06: Optimization Olympics
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├── extras/ # Additional demos and variants (see extras/README.md)
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└── data_manager.py # Shared dataset management utility
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```
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## The Journey
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```
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Module 01-03 Module 04-06 Module 08-09
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│ │ │
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▼ ▼ ▼
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┌─────────┐ ┌─────────┐ ┌─────────┐
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│ MS 01 │ ──────► │ MS 02 │ ─────────► │ MS 03 │
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│ 1957 │ │ 1969 │ │ 1986 │
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│ Forward │ │ XOR │ │ Backprop│
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└─────────┘ └─────────┘ └─────────┘
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│
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Module 11-13 │ Module 09
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│ ▼ │
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▼ ┌─────────┐ │
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┌──────────┐ │ MS 04 │◄─┘
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│ MS 05 │ │ 1998 │
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│ 2017 │ │ CNN │
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│ Attention│ └─────────┘
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└──────────┘
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│
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│ Module 14-19
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▼
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┌─────────┐
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│ MS 06 │
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│ 2018 │
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│ Optimize│
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└─────────┘
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```
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## Success Criteria
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Each milestone has specific success criteria. Passing means your implementation is correct:
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- **Milestone 01**: Forward pass produces reasonable outputs
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- **Milestone 02**: Demonstrates XOR is unsolvable with single layer (75% max accuracy)
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- **Milestone 03**: Part 1 solves XOR (100% accuracy), Part 2 achieves 85%+ on TinyDigits
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- **Milestone 04**: TinyDigits achieves 90%+ accuracy with CNN
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- **Milestone 05**: Pass all three attention challenges (95%+ accuracy)
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- **Milestone 06**: Part 1 completes optimization pipeline, Part 2 shows KV cache speedup
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## Troubleshooting
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If a milestone fails:
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1. Check that all required modules are completed: `tito module status`
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2. Run the module tests: `tito test <module_number>`
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3. Look at the specific error message for debugging hints
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4. Review the milestone's docstring for implementation requirements
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