# TinyTorch for Instructors: Complete ML Systems Course

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<h3 style="margin: 0 0 0.5rem 0; color: #155724;">✅ Classroom Integration Available</h3>
<p style="margin: 0; color: #155724;">TinyTorch includes complete <a href="https://nbgrader.readthedocs.io/" style="color: #155724; text-decoration: underline; font-weight: bold;">NBGrader</a> integration with automated grading workflows. See the <a href="../instructor-guide.html" style="color: #155724; font-weight: bold;">Complete Instructor Guide</a> for setup, grading rubrics, and sample solutions.</p>
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<strong>📖 Course Vision:</strong> This page describes the planned TinyTorch classroom experience.<br>
<strong>📖 For Current Usage:</strong> Students should follow the <a href="../student-workflow.html">Student Workflow</a> guide.
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<h2 style="margin: 0 0 1rem 0; color: #495057;">🏫 Planned: Turn-Key ML Systems Education</h2>
<p style="font-size: 1.1rem; margin: 0; color: #6c757d;">Transform students from framework users to systems engineers</p>
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**Vision:** Replace black-box API courses with deep systems understanding. Students will build neural networks from scratch, understand every operation, and graduate job-ready for ML engineering roles.

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## 🎯 Planned Course Infrastructure

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<h4 style="margin: 0 0 1rem 0; color: #0056b3;">Planned Features: Production-Ready Course Materials</h4>
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<li><strong>Three-tier progression</strong> (20 modules) with [NBGrader](https://nbgrader.readthedocs.io/) integration</li>
<li><strong>Automated grading</strong> for immediate feedback</li>
<li><strong>Professional CLI tools</strong> for development workflow</li>
<li><strong>Real datasets</strong> (CIFAR-10, text generation)</li>
</ul>
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<li><strong>Complete instructor guide</strong> with setup & grading ([available now](../instructor-guide.md))</li>
<li><strong>Flexible pacing</strong> (14-18 weeks depending on depth)</li>
<li><strong>Industry practices</strong> (Git, testing, documentation)</li>
<li><strong>Academic foundation</strong> from university research</li>
</ul>
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**Planned Course Duration:** 14-16 weeks (flexible pacing)
**Student Outcome:** Complete ML framework supporting vision AND language models

**Current Status:** Complete NBGrader integration available! See the [Instructor Guide](../instructor-guide.md) for setup, grading workflows, and sample solutions.

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## 🌟 Why TinyTorch for Your Classroom

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<h4 style="margin: 0 0 1rem 0; color: #2e7d32;">🎯 Deep Learning Outcomes</h4>
<p style="margin: 0 0 0.5rem 0; font-weight: 600;">Students build neural networks from scratch</p>
<ul style="margin: 0; font-size: 0.9rem; color: #64748b;">
<li>Graduates understand deep systems architecture</li>
<li>Can debug ML issues from first principles</li>
<li>Prepared for ML engineering roles</li>
<li>Confident implementing novel architectures</li>
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<h4 style="margin: 0 0 1rem 0; color: #f57c00;">⚡ Zero-Setup Teaching</h4>
<p style="margin: 0 0 0.5rem 0; font-weight: 600;">30-minute instructor setup, then focus on teaching</p>
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<li><strong>NBGrader integration</strong>: Automated grading & feedback</li>
<li><strong>One-command workflows</strong>: Generate, release, collect assignments</li>
<li><strong>Progress dashboards</strong>: Track all students at a glance</li>
<li><strong>Flexible pacing</strong>: Adapt to your semester schedule</li>
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<h4 style="margin: 0 0 1rem 0; color: #7b1fa2;">🏆 Industry-Standard Workflow</h4>
<p style="margin: 0 0 0.5rem 0; font-weight: 600;">Students learn professional ML engineering practices</p>
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<li><strong>Git workflow</strong>: Feature branches, commits, merges</li>
<li><strong>CLI tools</strong>: Professional development environment</li>
<li><strong>Testing culture</strong>: Every implementation immediately validated</li>
<li><strong>Documentation</strong>: Clear code, explanations, insights</li>
</ul>
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<h4 style="margin: 0 0 1rem 0; color: #0277bd;">🔬 Deep Systems Understanding</h4>
<p style="margin: 0 0 0.5rem 0; font-weight: 600;">Beyond APIs: Students understand how ML really works</p>
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<li><strong>Memory analysis</strong>: Profile and optimize resource usage</li>
<li><strong>Performance insights</strong>: Understand computational complexity</li>
<li><strong>Production context</strong>: How PyTorch/TensorFlow actually work</li>
<li><strong>Systems thinking</strong>: Architecture, scaling, optimization</li>
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## Course Module Overview

The TinyTorch course consists of 20 progressive modules organized into learning stages.

**📖 See [Complete Course Structure](../chapters/00-introduction.md)** for detailed module descriptions, learning objectives, and prerequisites for each module.

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## Academic Learning Goals

**What Students Will Achieve:**
- Build deep systems understanding through implementation
- Bridge gap between ML theory and engineering practice
- Prepare for real-world ML systems challenges
- Enable research into novel architectures and optimizations

**Core Capabilities Developed:**
- Implement neural networks from scratch
- Understand autograd and backpropagation deeply
- Optimize models for production deployment
- Build complete frameworks supporting vision and language

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## 🚀 Quick Start for Instructors

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<h3 style="margin: 0 0 1rem 0; text-align: center; color: #495057;">⏱️ 30 Minutes to Teaching-Ready Course</h3>
<p style="text-align: center; margin: 0 0 1.5rem 0; color: #6c757d;">Three simple steps to transform your ML teaching</p>

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<h4 style="color: #495057; margin: 0 0 0.5rem 0;">1️⃣ Clone & Setup (10 min)</h4>
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git clone TinyTorch<br>
cd TinyTorch<br>
source .venv/bin/activate<br>
pip install -r requirements.txt
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<p style="font-size: 0.9rem; margin: 0; color: #6c757d;">One-time environment setup</p>
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<h4 style="color: #495057; margin: 0 0 0.5rem 0;">2️⃣ Initialize Course (10 min)</h4>
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tito nbgrader init<br>
tito module status --comprehensive
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<p style="font-size: 0.9rem; margin: 0; color: #6c757d;">NBGrader integration & health check</p>
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<h4 style="color: #495057; margin: 0 0 0.5rem 0;">3️⃣ First Assignment (10 min)</h4>
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tito nbgrader generate 01_tensor<br>
tito nbgrader release 01_tensor
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<p style="font-size: 0.9rem; margin: 0; color: #6c757d;">Ready to distribute to students!</p>
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<a href="../instructor-guide.html" style="display: inline-block; background: #007bff; color: white; padding: 0.5rem 1rem; border-radius: 0.25rem; text-decoration: none; font-weight: 500; margin-right: 1rem;">📖 Complete Instructor Guide</a>
<a href="ta-guide.html" style="display: inline-block; background: #28a745; color: white; padding: 0.5rem 1rem; border-radius: 0.25rem; text-decoration: none; font-weight: 500;">👥 TA Guide</a>
<a href="../testing-framework.html" style="display: inline-block; background: #28a745; color: white; padding: 0.5rem 1rem; border-radius: 0.25rem; text-decoration: none; font-weight: 500;">🧪 Testing Framework Guide</a>
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## 📋 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

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## Instructor Resources

### Essential Documentation
- **[Complete Instructor Guide](../instructor-guide.md)** - 30-minute setup, grading rubrics, sample solutions, common errors
- **[TA Guide](ta-guide.md)** - Common student errors, debugging strategies, office hour patterns
- Module-specific teaching notes in each ABOUT.md file
- [Course Structure](../chapters/00-introduction.md) - Full curriculum overview
- [Student Workflow](../student-workflow.md) - Essential development cycle

### Support Tools
- `tito module status --comprehensive` - System health dashboard
- `tito nbgrader status` - Assignment tracking
- `tito nbgrader report` - Grade export

### Community
- GitHub Issues for technical support
- Instructor discussion forum (coming soon)
- Regular updates and improvements

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## 📞 Next Steps

1. **📖 Review [Course Structure](../chapters/00-introduction.md)** for complete curriculum overview
2. **🚀 Explore [Student Workflow](../student-workflow.md)** to understand the development cycle
3. **💻 Set up your environment** using the [Quick Start Guide](../quickstart-guide.md)
4. **📧 Contact us** via GitHub Issues for instructor support

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*Ready to teach the most comprehensive ML systems course? Let's build something amazing together!* 🎓