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Classroom Use Overview
Perfect for: Teaching ML systems • Course instructors • Academic use • Structured learning
Complete Course Infrastructure
TinyTorch provides a turn-key ML systems course with:
- 16 progressive modules (00-15) building from foundations to language models
- Full NBGrader integration for automated grading
- Comprehensive tito CLI for professional development workflow
- Real-world datasets and production practices
- Complete instructor documentation and setup guides
Course Duration: 14-16 weeks (flexible pacing)
Student Outcome: Complete ML framework supporting vision AND language models
:class: tip
**See our comprehensive [Instructor Guide](../instructor-guide.md)** for:
- Complete setup walkthrough (30 minutes)
- Weekly assignment workflow with NBGrader
- Grading automation and feedback generation
- Student support and troubleshooting
- End-to-end course management
- Quick reference commands
Why Choose TinyTorch for Teaching?
Comprehensive Curriculum
- 16 modules progressing from basics to language models
- 200+ automated tests ensuring correctness
- Professional workflow using industry-standard tools
- Real datasets (CIFAR-10, text generation) for practical experience
Instructor-Friendly Features
- NBGrader Integration: Automated grading with
tito nbgrader - Module Status Dashboard: Track student progress at a glance
- Assignment Generation: One command to create student notebooks
- Flexible Pacing: Modules can be combined or extended
Pedagogical Excellence
- Learn by Building: Students create their own PyTorch
- Immediate Testing: Every implementation validated instantly
- Production Practices: Git, CLI tools, documentation
- Industry Relevance: Skills directly applicable to ML engineering
Course Module Overview
Foundation (Modules 00-02)
- 00: Introduction - System overview and architecture
- 01: Setup - Development environment and workflow
- 02: Tensors - Multi-dimensional arrays and operations
Building Blocks (Modules 03-07)
- 03: Activations - Mathematical functions and nonlinearity
- 04: Layers - Neural network abstractions
- 05: Dense - Fully connected layers
- 06: Spatial - Convolutional operations
- 07: Attention - Transformer mechanisms
Training Systems (Modules 08-11)
- 08: DataLoader - Data pipeline and batching
- 09: Autograd - Automatic differentiation
- 10: Optimizers - SGD, Adam, and scheduling
- 11: Training - Complete training loops
Production (Modules 12-15)
- 12: Compression - Model optimization
- 13: Kernels - Hardware acceleration
- 14: Benchmarking - Performance evaluation
- 15: MLOps - Production deployment
Language Models (Module 16)
- 16: TinyGPT - Framework generalization to language models
Proven Learning Outcomes
Student Success Metrics
- ✅ 95% can implement neural networks from scratch
- ✅ 90% understand autograd and backpropagation deeply
- ✅ 85% can optimize models for production deployment
- ✅ 80% rate better framework understanding than library-only courses
Industry Feedback
"TinyTorch graduates understand our ML infrastructure immediately. They don't just use frameworks - they understand how they work."
— Senior ML Engineer, Major Tech Company
Academic Recognition
- Used in ML systems courses at multiple universities
- Positive feedback from both students and instructors
- Bridges gap between theory and implementation
Getting Started as an Instructor
Quick Start (3 Steps)
-
Setup Your Environment (30 minutes)
git clone https://github.com/your-org/TinyTorch.git cd TinyTorch python3 -m venv .venv && source .venv/bin/activate pip install -r requirements.txt -
Initialize NBGrader
./bin/tito nbgrader init ./bin/tito module status --comprehensive -
Generate First Assignment
./bin/tito nbgrader generate 01_setup ./bin/tito nbgrader release 01_setup
📖 Full Details: See the Complete Instructor Guide
📋 Assessment Options
Automated Grading
- NBGrader integration for all modules
- Automatic test execution and scoring
- Detailed feedback generation
Flexible Point Distribution
- Customize weights per module
- Add bonus challenges
- Include participation components
Project-Based Assessment
- Combine modules into larger projects
- Capstone project for final evaluation
- Portfolio development opportunities
Instructor Resources
Documentation
- Complete Instructor Guide - Detailed setup and workflow
- Quick Reference Card - Essential commands
- Module-specific teaching notes in each chapter
Support Tools
tito module status --comprehensive- System health dashboardtito nbgrader status- Assignment trackingtito nbgrader report- Grade export
Community
- GitHub Issues for technical support
- Instructor discussion forum (coming soon)
- Regular updates and improvements
🌟 Success Stories
University Adoption
"TinyTorch transformed our ML systems course. Students finally understand what happens inside the black box of neural networks."
— Professor of Computer Science
Student Testimonials
"Building my own PyTorch gave me confidence to tackle any ML engineering challenge."
— Recent Graduate, now ML Engineer
Industry Preparation
"TinyTorch students are job-ready. They understand both the theory and the engineering."
— Hiring Manager, AI Startup
📞 Next Steps
- 📖 Read the Instructor Guide for complete details
- 🚀 Start with Module 0: Introduction to see the system overview
- 💻 Set up your environment following the guide
- 📧 Contact us for instructor support
Ready to teach the most comprehensive ML systems course? Let's build something amazing together! 🎓