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Paper improvements: - Add differentiated time estimates (60-80h experienced, 100-120h typical, 140-180h struggling) - Moderate cognitive load claims with hedging language and empirical validation notes - Add ML Systems Research subsection with citations (Baydin AD survey, Chen gradient checkpointing, TVM, FlashAttention) - Add comprehensive Threats to Validity section (selection bias, single institution, demand characteristics, no control group, maturation, assessment validity) - Define jargon (monkey-patching) at first use with clear explanation Documentation updates: - Restructure TITO CLI docs into dedicated section (overview, modules, milestones, data, troubleshooting) - Update student workflow guide and quickstart guide - Remove deprecated files (testing-framework.md, tito-essentials.md) - Update module template and testing architecture docs Repository cleanup: - Remove temporary review files (ADDITIONAL_REVIEWS.md, EDTECH_OPENSOURCE_REVIEWS.md, TA_STRUGGLING_STUDENT_REVIEWS.md, etc.) - Remove temporary development planning docs - Update demo GIFs and configurations
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Module ABOUT.md Template
This template defines the standardized structure for all module ABOUT.md files used in the Jupyter Book site.
Standard Structure
---
title: "[Module Title]"
description: "[Brief description]"
difficulty: "[⭐⭐⭐⭐]"
time_estimate: "[X-Y hours]"
prerequisites: []
next_steps: []
learning_objectives: []
---
# [NN]. [Module Title]
**[TIER]** | Difficulty: ⭐⭐⭐⭐ (X/4) | Time: X-Y hours
## Overview
[2-3 sentence overview explaining what this module builds and why it matters]
## Learning Objectives
By the end of this module, you will be able to:
- **[Objective 1]**: [Description]
- **[Objective 2]**: [Description]
- **[Objective 3]**: [Description]
- **[Objective 4]**: [Description]
- **[Objective 5]**: [Description]
## Build → Use → [Analyze/Optimize/Reflect]
This module follows TinyTorch's **Build → Use → [Third Stage]** framework:
1. **Build**: [What students implement]
2. **Use**: [How they apply it]
3. **[Third Stage]**: [Deeper engagement - varies by module]
## Implementation Guide
### [Main Component Name]
```python
# Example code showing key functionality
[Additional Components]
[More implementation examples]
Getting Started
Prerequisites
Ensure you understand the [foundations]:
# Activate TinyTorch environment
source bin/activate-tinytorch.sh
# Verify prerequisite modules
tito test --module [prerequisite1]
tito test --module [prerequisite2]
Development Workflow
- Open the development file:
modules/[NN]_[modulename]/[modulename]_dev.py - Implement [component 1]: [Description]
- Build [component 2]: [Description]
- Create [component 3]: [Description]
- Add [component 4]: [Description]
- Export and verify:
tito module complete [NN] && tito test --module [modulename]
Testing
Comprehensive Test Suite
Run the full test suite to verify [module] functionality:
# TinyTorch CLI (recommended)
tito test --module [modulename]
# Direct pytest execution
python -m pytest tests/ -k [modulename] -v
Test Coverage Areas
- ✅ [Test area 1]: [Description]
- ✅ [Test area 2]: [Description]
- ✅ [Test area 3]: [Description]
- ✅ [Test area 4]: [Description]
- ✅ [Test area 5]: [Description]
Inline Testing & [Analysis Type]
The module includes comprehensive [validation type]:
# Example inline test output
🔬 Unit Test: [Component]...
✅ [Test result 1]
✅ [Test result 2]
📈 Progress: [Component] ✓
Manual Testing Examples
from [modulename]_dev import [Component]
# Example usage
Systems Thinking Questions
Real-World Applications
- [Application 1]: [Description]
- [Application 2]: [Description]
- [Application 3]: [Description]
- [Application 4]: [Description]
[Mathematical/Technical] Foundations
- [Concept 1]: [Description]
- [Concept 2]: [Description]
- [Concept 3]: [Description]
- [Concept 4]: [Description]
[Theory/Performance] Characteristics
- [Characteristic 1]: [Description]
- [Characteristic 2]: [Description]
- [Characteristic 3]: [Description]
- [Characteristic 4]: [Description]
Ready to Build?
[2-3 paragraph motivational conclusion explaining why this module matters and what students will achieve]
Choose your preferred way to engage with this module:
```{grid-item-card} 🚀 Launch Binder
:link: https://mybinder.org/v2/gh/mlsysbook/TinyTorch/main?filepath=modules/[NN]_[modulename]/[modulename]_dev.ipynb
:class-header: bg-light
Run this module interactively in your browser. No installation required!
```
```{grid-item-card} ⚡ Open in Colab
:link: https://colab.research.google.com/github/mlsysbook/TinyTorch/blob/main/modules/[NN]_[modulename]/[modulename]_dev.ipynb
:class-header: bg-light
Use Google Colab for GPU access and cloud compute power.
```
```{grid-item-card} 📖 View Source
:link: https://github.com/mlsysbook/TinyTorch/blob/main/modules/[NN]_[modulename]/[modulename]_dev.py
:class-header: bg-light
Browse the Python source code and understand the implementation.
```
:class: tip
**Binder sessions are temporary!** Download your completed notebook when done, or switch to local development for persistent work.
```
Required Sections
All modules MUST include:
- Frontmatter (YAML metadata)
- Title with tier/difficulty/time
- Overview
- Learning Objectives
- Build → Use → [Third Stage]
- Implementation Guide
- Getting Started (Prerequisites + Development Workflow)
- Testing (Comprehensive Test Suite + Test Coverage + Inline Testing + Manual Examples)
- Systems Thinking Questions (Real-World Applications + Foundations + Characteristics)
- Ready to Build? (Motivational conclusion)
- Launch Binder/Colab/Source grid
- Save Your Progress admonition
- Previous/Next navigation
Optional Sections
- "Why This Matters" (can be integrated into Overview or Systems Thinking)
- Additional implementation examples
- Extended mathematical foundations