mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2025-12-05 19:17:52 -06:00
Standardize emoji usage across all site pages for professional consistency
- Removed emojis from all section headers (## and ###) - Reduced emojis in body text and callout boxes - Standardized link references (removed emoji prefixes) - Maintained professional tone while keeping content accessible - Updated quickstart-guide, student-workflow, tito-essentials, faq, datasets, community, resources, testing-framework, learning-progress, checkpoint-system, and all chapter files
This commit is contained in:
Vijay Janapa Reddi
@@ -36,7 +36,7 @@ When you implement your own tensor operations, write your own autograd, build yo
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---
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## 🎯 Core Learning Concepts
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## Core Learning Concepts
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<div style="background: #f7fafc; border: 1px solid #e2e8f0; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0;">
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@@ -143,7 +143,7 @@ output = model(input) # YOU know exactly how this works
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## What You'll Achieve: Tier-by-Tier Mastery
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### 🏗️ After Foundation Tier (Modules 01-07)
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### After Foundation Tier (Modules 01-07)
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Build a complete neural network framework from mathematical first principles:
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```python
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@@ -167,13 +167,13 @@ for batch in dataloader: # Your data management
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**🎯 Foundation Achievement**: 95%+ accuracy on MNIST using 100% your own mathematical implementations
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### 🏛️ After Architecture Tier (Modules 08-13)
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### After Architecture Tier (Modules 08-13)
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- **Computer Vision Mastery**: CNNs achieving 75%+ accuracy on CIFAR-10 with YOUR convolution implementations
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- **Language Understanding**: Transformers generating coherent text using YOUR attention mechanisms
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- **Universal Architecture**: Discover why the SAME mathematical principles work for vision AND language
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- **AI Breakthrough Recreation**: Implement the architectures that created the modern AI revolution
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### ⚡ After Optimization Tier (Modules 14-20)
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### After Optimization Tier (Modules 14-20)
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- **Production Performance**: Systems optimized for <100ms inference latency using YOUR profiling tools
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- **Memory Efficiency**: Models compressed to 25% original size with YOUR quantization implementations
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- **Hardware Acceleration**: Kernels achieving 10x speedups through YOUR vectorization techniques
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@@ -185,20 +185,20 @@ for batch in dataloader: # Your data management
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TinyTorch's three-tier structure follows the actual historical progression of machine learning breakthroughs:
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### 🏗️ Foundation Era (1980s-1990s) → Foundation Tier
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### Foundation Era (1980s-1990s) → Foundation Tier
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**The Beginning**: Mathematical foundations that started it all
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- **1986 Breakthrough**: Backpropagation enables multi-layer networks
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- **Your Implementation**: Build automatic differentiation and gradient-based optimization
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- **Historical Milestone**: Train MLPs to 95%+ accuracy on MNIST using YOUR autograd engine
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### 🏛️ Architecture Era (1990s-2010s) → Architecture Tier
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### Architecture Era (1990s-2010s) → Architecture Tier
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**The Revolution**: Specialized architectures for vision and language
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- **1998 Breakthrough**: CNNs revolutionize computer vision (LeCun's LeNet)
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- **2017 Breakthrough**: Transformers unify vision and language ("Attention is All You Need")
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- **Your Implementation**: Build CNNs achieving 75%+ on CIFAR-10, then transformers for text generation
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- **Historical Milestone**: Recreate both revolutions using YOUR spatial and attention implementations
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### ⚡ Optimization Era (2010s-Present) → Optimization Tier
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### Optimization Era (2010s-Present) → Optimization Tier
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**The Engineering**: Production systems that scale to billions of users
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- **2020s Breakthrough**: Efficient inference enables real-time LLMs (GPT, ChatGPT)
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- **Your Implementation**: Build KV-caching, quantization, and production optimizations
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@@ -278,7 +278,7 @@ After each tier, you become the team member who:
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---
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## 🚀 Start Your Journey
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## Start Your Journey
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<div style="background: #f8f9fa; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0; text-align: center;">
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<h3 style="margin: 0 0 1rem 0; color: #495057;">Begin Building ML Systems</h3>
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@@ -288,9 +288,9 @@ After each tier, you become the team member who:
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</div>
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**Next Steps**:
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- **New to TinyTorch**: Start with [Quick Start Guide](../quickstart-guide.html) for immediate hands-on experience
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- **Ready to Commit**: Begin [Module 01: Setup](01-setup.html) to configure your development environment
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- **Teaching a Course**: Review [Instructor Guide](../usage-paths/classroom-use.html) for classroom integration
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- **New to TinyTorch**: Start with [Quick Start Guide](../quickstart-guide.md) for immediate hands-on experience
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- **Ready to Commit**: Begin [Module 01: Tensor](../../modules/01_tensor/ABOUT.md) to start building
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- **Teaching a Course**: Review [Instructor Guide](../usage-paths/classroom-use.md) for classroom integration
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```{admonition} Your Three-Tier Journey Awaits
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:class: tip
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@@ -303,11 +303,11 @@ By completing all three tiers, you'll have built a complete ML framework that ri
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All using code you wrote yourself, from mathematical first principles to production optimization.
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```
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**📖 Want to understand the pedagogical narrative behind this structure?** See [The Learning Journey](learning-journey.html) to understand WHY modules flow this way and HOW they build on each other through a six-act learning story.
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**📖 Want to understand the pedagogical narrative behind this structure?** See [The Learning Journey](learning-journey.md) to understand WHY modules flow this way and HOW they build on each other through a six-act learning story.
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---
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### 🏗️ FOUNDATION TIER (Modules 01-07)
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### Foundation Tier (Modules 01-07)
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**Building Blocks of ML Systems • 6-8 weeks • All Prerequisites for Neural Networks**
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<div style="background: #f8f9fd; border: 1px solid #e0e7ff; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0;">
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@@ -342,7 +342,7 @@ All using code you wrote yourself, from mathematical first principles to product
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---
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### 🏛️ ARCHITECTURE TIER (Modules 08-13)
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### Architecture Tier (Modules 08-13)
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**Modern AI Algorithms • 4-6 weeks • Vision + Language Architectures**
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<div style="background: #fef7ff; border: 1px solid #f3e8ff; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0;">
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@@ -376,7 +376,7 @@ All using code you wrote yourself, from mathematical first principles to product
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---
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### ⚡ OPTIMIZATION TIER (Modules 14-20)
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### Optimization Tier (Modules 14-19)
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**Production & Performance • 4-6 weeks • Deploy and Scale ML Systems**
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<div style="background: #f0fdfa; border: 1px solid #a7f3d0; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0;">
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@@ -411,7 +411,7 @@ All using code you wrote yourself, from mathematical first principles to product
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---
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## 🎯 Learning Path Recommendations
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## Learning Path Recommendations
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### Choose Your Learning Style
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@@ -7,9 +7,9 @@
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## What This Page Is About
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This page tells the **pedagogical story** behind TinyTorch's module progression. While other pages explain:
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- **WHAT you'll build** ([Three-Tier Structure](00-introduction.html)) - organized module breakdown
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- **WHEN in history** ([Milestones](milestones.html)) - recreating ML breakthroughs
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- **WHERE you are** ([Progress Tracking](../learning-progress.html)) - capability checkpoints
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- **WHAT you'll build** ([Three-Tier Structure](00-introduction.md)) - organized module breakdown
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- **WHEN in history** ([Milestones](milestones.md)) - recreating ML breakthroughs
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- **WHERE you are** ([Progress Tracking](../learning-progress.md)) - capability checkpoints
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This page explains **WHY modules flow this way** - the learning narrative that transforms 20 individual modules into a coherent journey from mathematical foundations to production AI systems.
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@@ -312,7 +312,7 @@ As you progress through TinyTorch, you advance along **two dimensions simultaneo
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**Understanding Both Dimensions**: The **Acts** explain WHY you're building each component (pedagogical progression). The **Milestones** prove WHAT you've built actually works (historical validation).
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**📖 See [Journey Through ML History](milestones.html)** for complete milestone details and how to run them.
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**📖 See [Journey Through ML History](milestones.md)** for complete milestone details and how to run them.
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---
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@@ -346,7 +346,7 @@ The learning journey also maps to **21 capability checkpoints** you can track:
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- Checkpoint 19: Competitive benchmarking ✓
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- Checkpoint 20: Complete systems ✓
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**📖 See [Progress Tracking](../learning-progress.html)** to monitor your capability development.
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**📖 See [Progress Tracking](../learning-progress.md)** to monitor your capability development.
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---
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@@ -527,7 +527,7 @@ Typical time estimates (varies by background):
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- Act V → Systems (2024)
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- Act VI → TinyGPT (complete)
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**📖 See [Milestones](milestones.html)** for details.
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**📖 See [Milestones](milestones.md)** for details.
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---
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@@ -543,10 +543,10 @@ Typical time estimates (varies by background):
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</div>
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**Related Resources**:
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- **[Three-Tier Structure](00-introduction.html)** - Organized module breakdown with time estimates
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- **[Journey Through ML History](milestones.html)** - Historical milestones you'll recreate
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- **[Progress Tracking](../learning-progress.html)** - Monitor your capability development
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- **[Quick Start Guide](../quickstart-guide.html)** - Hands-on setup and first module
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- **[Three-Tier Structure](00-introduction.md)** - Organized module breakdown with time estimates
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- **[Journey Through ML History](milestones.md)** - Historical milestones you'll recreate
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- **[Progress Tracking](../learning-progress.md)** - Monitor your capability development
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- **[Quick Start Guide](../quickstart-guide.md)** - Hands-on setup and first module
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---
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@@ -1,23 +1,23 @@
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# 🏆 Journey Through ML History
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# Journey Through ML History
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**Experience the evolution of AI by rebuilding history's most important breakthroughs with YOUR TinyTorch implementations.**
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---
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## 🎯 What Are Milestones?
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## What Are Milestones?
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Milestones are **proof-of-mastery demonstrations** that showcase what you can build after completing specific modules. Each milestone recreates a historically significant ML achievement using YOUR implementations.
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### Why This Approach?
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- 🧠 **Deep Understanding**: Experience the actual challenges researchers faced
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- 📈 **Progressive Learning**: Each milestone builds on previous foundations
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- 🏆 **Real Achievements**: Not toy examples - these are historically significant breakthroughs
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- 🔧 **Systems Thinking**: Understand WHY each innovation mattered for ML systems
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- **Deep Understanding**: Experience the actual challenges researchers faced
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- **Progressive Learning**: Each milestone builds on previous foundations
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- **Real Achievements**: Not toy examples - these are historically significant breakthroughs
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- **Systems Thinking**: Understand WHY each innovation mattered for ML systems
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---
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## 🎯 Two Dimensions of Your Progress
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## Two Dimensions of Your Progress
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As you build TinyTorch, you're progressing along **TWO dimensions simultaneously**:
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@@ -30,7 +30,7 @@ As you build TinyTorch, you're progressing along **TWO dimensions simultaneously
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**Act V (14-19)**: Production systems - optimization and deployment
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**Act VI (20)**: Complete integration - unified AI systems
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**📖 See [The Learning Journey](learning-journey.html)** for the complete pedagogical narrative explaining WHY modules flow this way.
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See [The Learning Journey](learning-journey.md) for the complete pedagogical narrative explaining WHY modules flow this way.
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### Historical Dimension (Milestones): What You CAN Build
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@@ -45,20 +45,20 @@ As you build TinyTorch, you're progressing along **TWO dimensions simultaneously
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| Learning Act | Unlocked Milestone | Proof of Mastery |
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|--------------|-------------------|------------------|
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| **Act I: Foundation (01-04)** | 🧠 1957 Perceptron | Your Linear layer recreates history |
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| **Act II: Learning (05-07)** | ⚡ 1969 XOR + 🔢 1986 MLP | Your autograd enables training (95%+ MNIST) |
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| **Act III: Data & Scale (08-09)** | 🖼️ 1998 CNN | Your Conv2d achieves 75%+ on CIFAR-10 |
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| **Act IV: Language (10-13)** | 🤖 2017 Transformers | Your attention generates coherent text |
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| **Act V: Production (14-18)** | ⚡ 2018 MLPerf | Your optimizations achieve production speed |
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| **Act VI: Integration (19-20)** | 🏆 Benchmarking + Capstone | Your complete framework competes |
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| **Act I: Foundation (01-04)** | 1957 Perceptron | Your Linear layer recreates history |
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| **Act II: Learning (05-07)** | 1969 XOR + 1986 MLP | Your autograd enables training (95%+ MNIST) |
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| **Act III: Data & Scale (08-09)** | 1998 CNN | Your Conv2d achieves 75%+ on CIFAR-10 |
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| **Act IV: Language (10-13)** | 2017 Transformers | Your attention generates coherent text |
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| **Act V: Production (14-18)** | 2018 MLPerf | Your optimizations achieve production speed |
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| **Act VI: Integration (19-20)** | Benchmarking + Capstone | Your complete framework competes |
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**Understanding Both Dimensions**: The **Acts** explain WHY you're building each component (pedagogical progression). The **Milestones** prove WHAT you've built works (historical validation). Together, they show you're not just completing exercises - you're building something real.
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---
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## 📅 The Timeline
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## The Timeline
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### 🧠 01. Perceptron (1957) - Rosenblatt
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### 01. Perceptron (1957) - Rosenblatt
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**After Modules 02-04**
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@@ -88,7 +88,7 @@ python 02_rosenblatt_trained.py # See the solution (trained)
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---
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### ⚡ 02. XOR Crisis (1969) - Minsky & Papert
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### 02. XOR Crisis (1969) - Minsky & Papert
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**After Modules 02-06**
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@@ -118,7 +118,7 @@ python 02_xor_solved.py # Hidden layers solve it!
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---
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### 🔢 03. MLP Revival (1986) - Backpropagation Era
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### 03. MLP Revival (1986) - Backpropagation Era
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**After Modules 02-08**
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@@ -148,7 +148,7 @@ python 02_rumelhart_mnist.py # Full MNIST
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---
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### 🖼️ 04. CNN Revolution (1998) - LeCun's Breakthrough
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### 04. CNN Revolution (1998) - LeCun's Breakthrough
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**After Modules 02-09** • **🎯 North Star Achievement**
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@@ -178,7 +178,7 @@ python 02_lecun_cifar10.py # CIFAR-10 @ 75%+ accuracy
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---
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### 🤖 05. Transformer Era (2017) - Attention Revolution
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### 05. Transformer Era (2017) - Attention Revolution
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**After Modules 02-13**
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@@ -208,7 +208,7 @@ python 02_vaswani_dialogue.py # Multi-turn dialogue
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---
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### ⚡ 06. MLPerf Era (2018) - The Optimization Revolution
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### 06. MLPerf Era (2018) - The Optimization Revolution
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**After Modules 14-18**
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@@ -239,7 +239,7 @@ python 03_generation_opts.py # Speed up inference (cache + batch)
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---
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## 🎓 Learning Philosophy
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## Learning Philosophy
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### Progressive Capability Building
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@@ -263,7 +263,7 @@ Each milestone teaches critical systems thinking:
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---
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## 🚀 How to Use Milestones
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## How to Use Milestones
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### 1. Complete Prerequisites
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@@ -302,7 +302,7 @@ Each milestone includes:
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---
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## 🎯 Quick Reference
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## Quick Reference
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### Milestone Prerequisites
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@@ -324,7 +324,7 @@ Each milestone includes:
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---
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## 📚 Further Learning
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## Further Learning
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After completing milestones, explore:
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@@ -335,7 +335,7 @@ After completing milestones, explore:
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---
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## 🌟 Why This Matters
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## Why This Matters
|
||||
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**Most courses teach you to USE frameworks.**
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**TinyTorch teaches you to UNDERSTAND them.**
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@@ -1,4 +1,4 @@
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# 🎯 TinyTorch Checkpoint System
|
||||
# TinyTorch Checkpoint System
|
||||
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<div style="background: #fff3cd; border: 1px solid #ffc107; padding: 1.5rem; border-radius: 0.5rem; margin: 2rem 0;">
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<h3 style="margin: 0 0 0.5rem 0; color: #856404;">📋 Optional Progress Tracking</h3>
|
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@@ -42,9 +42,9 @@ The TinyTorch checkpoint system provides optional infrastructure for capability
|
||||
|
||||
---
|
||||
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||||
## 🚀 The Five Major Checkpoints
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## The Five Major Checkpoints
|
||||
|
||||
### 🎯 Foundation
|
||||
### Foundation
|
||||
*Core ML primitives and environment setup*
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**Modules**: Setup • Tensors • Activations
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@@ -152,7 +152,7 @@ Every checkpoint completion unlocks a concrete capability:
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||||
|
||||
The checkpoint system provides comprehensive progress tracking and capability validation through automated testing infrastructure.
|
||||
|
||||
**📖 See [Essential Commands](tito-essentials.html)** for complete command reference and usage examples.
|
||||
**📖 See [Essential Commands](tito-essentials.md)** for complete command reference and usage examples.
|
||||
|
||||
### Integration with Development
|
||||
The checkpoint system connects directly to your actual development work:
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||||
@@ -248,7 +248,7 @@ The checkpoint progression **Foundation → Architecture → Training → Infere
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||||
- **Problem**: Modules don't work together due to missing dependencies
|
||||
- **Solution**: Verify prerequisite capabilities before testing advanced features
|
||||
|
||||
**📖 See [Essential Commands](tito-essentials.html)** for complete debugging command reference.
|
||||
**📖 See [Essential Commands](tito-essentials.md)** for complete debugging command reference.
|
||||
|
||||
### Checkpoint Test Structure
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||||
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||||
@@ -299,4 +299,4 @@ print("🏆 Foundation checkpoint PASSED")
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||||
- Analyze memory usage during testing
|
||||
- Identify bottlenecks in capability validation
|
||||
|
||||
**📖 See [Essential Commands](tito-essentials.html)** for complete command reference and advanced usage examples.
|
||||
**📖 See [Essential Commands](tito-essentials.md)** for complete command reference and advanced usage examples.
|
||||
@@ -1,42 +1,42 @@
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||||
# 🌍 Community Ecosystem
|
||||
# Community Ecosystem
|
||||
|
||||
**Building Together**
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Overview
|
||||
## Overview
|
||||
|
||||
TinyTorch is more than just a course—it's a growing community of students, educators, and ML engineers learning systems engineering from first principles.
|
||||
|
||||
---
|
||||
|
||||
## 📊 Community Platform (Coming Soon)
|
||||
## Community Platform (Coming Soon)
|
||||
|
||||
<div style="background: #e3f2fd; border: 2px solid #2196f3; padding: 1.5rem; border-radius: 0.5rem; margin: 2rem 0;">
|
||||
<h3 style="margin: 0 0 1rem 0; color: #1565c0;">🚧 Building Community Features</h3>
|
||||
<h3 style="margin: 0 0 1rem 0; color: #1565c0;">Building Community Features</h3>
|
||||
<p style="margin: 0; color: #1565c0;">We're creating live community features including activity dashboards, study partner matching, and real-time progress tracking. Stay tuned!</p>
|
||||
</div>
|
||||
|
||||
### Planned Features
|
||||
|
||||
**📊 Live Dashboard**
|
||||
**Live Dashboard**
|
||||
- Real-time community activity
|
||||
- Global learning progress
|
||||
- Module completion stats
|
||||
|
||||
**🤝 Connection Hub**
|
||||
**Connection Hub**
|
||||
- Find study partners
|
||||
- Join study groups
|
||||
- Connect with peers
|
||||
|
||||
**🌍 Global Reach**
|
||||
**Global Reach**
|
||||
- See who's learning worldwide
|
||||
- Geographic distribution
|
||||
- Community milestones
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Get Involved Now
|
||||
## Get Involved Now
|
||||
|
||||
**Learn Together**
|
||||
- Ask questions in [GitHub Discussions](https://github.com/harvard-edge/TinyTorch/discussions)
|
||||
@@ -55,4 +55,4 @@ TinyTorch is more than just a course—it's a growing community of students, edu
|
||||
|
||||
---
|
||||
|
||||
**Build ML systems. Learn together. Grow the community.** 🌍
|
||||
**Build ML systems. Learn together. Grow the community.**
|
||||
|
||||
@@ -14,7 +14,7 @@ TinyTorch uses a two-tier dataset approach:
|
||||
<div style="display: grid; grid-template-columns: 1fr 1fr; gap: 1.5rem; margin: 2rem 0;">
|
||||
|
||||
<div style="background: #e3f2fd; border: 1px solid #2196f3; padding: 1.5rem; border-radius: 0.5rem;">
|
||||
<h3 style="margin: 0 0 1rem 0; color: #1976d2;">📦 Shipped Datasets</h3>
|
||||
<h3 style="margin: 0 0 1rem 0; color: #1976d2;">Shipped Datasets</h3>
|
||||
<p style="margin: 0 0 1rem 0;"><strong>~350 KB total - Ships with repository</strong></p>
|
||||
<ul style="margin: 0; font-size: 0.9rem;">
|
||||
<li>Small enough to fit in Git (~1K samples each)</li>
|
||||
@@ -26,7 +26,7 @@ TinyTorch uses a two-tier dataset approach:
|
||||
</div>
|
||||
|
||||
<div style="background: #f3e5f5; border: 1px solid #9c27b0; padding: 1.5rem; border-radius: 0.5rem;">
|
||||
<h3 style="margin: 0 0 1rem 0; color: #7b1fa2;">⬇️ Downloaded Datasets</h3>
|
||||
<h3 style="margin: 0 0 1rem 0; color: #7b1fa2;">Downloaded Datasets</h3>
|
||||
<p style="margin: 0 0 1rem 0;"><strong>~180 MB - Auto-downloaded when needed</strong></p>
|
||||
<ul style="margin: 0; font-size: 0.9rem;">
|
||||
<li>Standard ML benchmarks (MNIST, CIFAR-10)</li>
|
||||
@@ -49,9 +49,9 @@ TinyTorch uses a two-tier dataset approach:
|
||||
|
||||
<div style="background: #fff5f5; border-left: 4px solid #e74c3c; padding: 1.5rem; margin: 1.5rem 0;">
|
||||
|
||||
**📍 Location**: `datasets/tinydigits/`
|
||||
**📊 Size**: ~310 KB
|
||||
**🎯 Used by**: Milestones 03 & 04 (MLP and CNN examples)
|
||||
**Location**: `datasets/tinydigits/`
|
||||
**Size**: ~310 KB
|
||||
**Used by**: Milestones 03 & 04 (MLP and CNN examples)
|
||||
|
||||
**Contents:**
|
||||
- 1,000 training samples
|
||||
@@ -82,9 +82,9 @@ X_train, y_train, X_test, y_test = load_tinydigits()
|
||||
|
||||
<div style="background: #f0fff4; border-left: 4px solid #22c55e; padding: 1.5rem; margin: 1.5rem 0;">
|
||||
|
||||
**📍 Location**: `datasets/tinytalks/`
|
||||
**📊 Size**: ~40 KB
|
||||
**🎯 Used by**: Milestone 05 (Transformer/GPT text generation)
|
||||
**Location**: `datasets/tinytalks/`
|
||||
**Size**: ~40 KB
|
||||
**Used by**: Milestone 05 (Transformer/GPT text generation)
|
||||
|
||||
**Contents:**
|
||||
- 350 Q&A pairs across 5 difficulty levels
|
||||
@@ -117,7 +117,7 @@ dataset = load_tinytalks()
|
||||
# Returns list of (question, answer) pairs
|
||||
```
|
||||
|
||||
**📖 See detailed documentation:** `datasets/tinytalks/README.md`
|
||||
See detailed documentation: `datasets/tinytalks/README.md`
|
||||
|
||||
</div>
|
||||
|
||||
@@ -131,9 +131,9 @@ These standard benchmarks download automatically when you run relevant milestone
|
||||
|
||||
<div style="background: #fffbeb; border-left: 4px solid #f59e0b; padding: 1.5rem; margin: 1.5rem 0;">
|
||||
|
||||
**📍 Downloads to**: `milestones/datasets/mnist/`
|
||||
**📊 Size**: ~10 MB (compressed)
|
||||
**🎯 Used by**: `milestones/03_1986_mlp/02_rumelhart_mnist.py`
|
||||
**Downloads to**: `milestones/datasets/mnist/`
|
||||
**Size**: ~10 MB (compressed)
|
||||
**Used by**: `milestones/03_1986_mlp/02_rumelhart_mnist.py`
|
||||
|
||||
**Contents:**
|
||||
- 60,000 training samples
|
||||
@@ -157,9 +157,9 @@ These standard benchmarks download automatically when you run relevant milestone
|
||||
|
||||
<div style="background: #fdf2f8; border-left: 4px solid #ec4899; padding: 1.5rem; margin: 1.5rem 0;">
|
||||
|
||||
**📍 Downloads to**: `milestones/datasets/cifar-10/`
|
||||
**📊 Size**: ~170 MB (compressed)
|
||||
**🎯 Used by**: `milestones/04_1998_cnn/02_lecun_cifar10.py`
|
||||
**Downloads to**: `milestones/datasets/cifar-10/`
|
||||
**Size**: ~170 MB (compressed)
|
||||
**Used by**: `milestones/04_1998_cnn/02_lecun_cifar10.py`
|
||||
|
||||
**Contents:**
|
||||
- 50,000 training samples
|
||||
@@ -186,28 +186,28 @@ These standard benchmarks download automatically when you run relevant milestone
|
||||
### Why These Specific Datasets?
|
||||
|
||||
**TinyDigits (not full MNIST):**
|
||||
- ✅ 100× faster training iterations
|
||||
- ✅ Ships with repo (no download)
|
||||
- ✅ Same conceptual challenges
|
||||
- ✅ Perfect for learning and debugging
|
||||
- 100× faster training iterations
|
||||
- Ships with repo (no download)
|
||||
- Same conceptual challenges
|
||||
- Perfect for learning and debugging
|
||||
|
||||
**TinyTalks (custom dataset):**
|
||||
- ✅ Designed for educational progression
|
||||
- ✅ Scaffolded difficulty levels
|
||||
- ✅ Character-level tokenization friendly
|
||||
- ✅ Engaging conversational format
|
||||
- Designed for educational progression
|
||||
- Scaffolded difficulty levels
|
||||
- Character-level tokenization friendly
|
||||
- Engaging conversational format
|
||||
|
||||
**MNIST (when scaling up):**
|
||||
- ✅ Industry standard benchmark
|
||||
- ✅ Validates your implementation
|
||||
- ✅ Comparable to published results
|
||||
- ✅ 95%+ accuracy is achievable milestone
|
||||
- Industry standard benchmark
|
||||
- Validates your implementation
|
||||
- Comparable to published results
|
||||
- 95%+ accuracy is achievable milestone
|
||||
|
||||
**CIFAR-10 (for CNN validation):**
|
||||
- ✅ Natural images (harder than digits)
|
||||
- ✅ RGB channels (multi-dimensional)
|
||||
- ✅ Standard CNN benchmark
|
||||
- ✅ 75%+ with basic CNN proves it works
|
||||
- Natural images (harder than digits)
|
||||
- RGB channels (multi-dimensional)
|
||||
- Standard CNN benchmark
|
||||
- 75%+ with basic CNN proves it works
|
||||
|
||||
---
|
||||
|
||||
@@ -249,12 +249,12 @@ conversations = load_tinytalks()
|
||||
|
||||
| Dataset | Size | Samples | Ships With Repo | Purpose |
|
||||
|---------|------|---------|-----------------|---------|
|
||||
| TinyDigits | 310 KB | 1,200 | ✅ Yes | Fast MLP/CNN iteration |
|
||||
| TinyTalks | 40 KB | 350 pairs | ✅ Yes | Transformer learning |
|
||||
| MNIST | 10 MB | 70,000 | ❌ Downloads | MLP validation |
|
||||
| CIFAR-10 | 170 MB | 60,000 | ❌ Downloads | CNN validation |
|
||||
| TinyDigits | 310 KB | 1,200 | Yes | Fast MLP/CNN iteration |
|
||||
| TinyTalks | 40 KB | 350 pairs | Yes | Transformer learning |
|
||||
| MNIST | 10 MB | 70,000 | Downloads | MLP validation |
|
||||
| CIFAR-10 | 170 MB | 60,000 | Downloads | CNN validation |
|
||||
|
||||
**Total shipped**: ~350 KB
|
||||
**Total shipped**: ~350 KB
|
||||
**Total with benchmarks**: ~180 MB
|
||||
|
||||
---
|
||||
@@ -283,27 +283,27 @@ conversations = load_tinytalks()
|
||||
|
||||
## Frequently Asked Questions
|
||||
|
||||
**Q: Why not use full MNIST from the start?**
|
||||
**Q: Why not use full MNIST from the start?**
|
||||
A: TinyDigits trains 100× faster, enabling rapid iteration during learning. MNIST validates your complete implementation later.
|
||||
|
||||
**Q: Can I use my own datasets?**
|
||||
**Q: Can I use my own datasets?**
|
||||
A: Absolutely! TinyTorch is a real framework—add your data loading code just like PyTorch.
|
||||
|
||||
**Q: Why ship datasets in Git?**
|
||||
**Q: Why ship datasets in Git?**
|
||||
A: 350 KB is negligible (smaller than many images), and it enables offline learning with instant iteration.
|
||||
|
||||
**Q: Where does CIFAR-10 download from?**
|
||||
**Q: Where does CIFAR-10 download from?**
|
||||
A: Official sources via `milestones/data_manager.py`, with integrity verification.
|
||||
|
||||
**Q: Can I skip the large downloads?**
|
||||
**Q: Can I skip the large downloads?**
|
||||
A: Yes! You can work through most milestones using only shipped datasets. Downloaded datasets are for validation milestones.
|
||||
|
||||
---
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **📖 [Milestones Guide](chapters/milestones.html)** - See how each dataset is used in historical achievements
|
||||
- **📖 [Student Workflow](student-workflow.html)** - Learn the development cycle
|
||||
- **📖 [Quick Start](quickstart-guide.html)** - Start building in 15 minutes
|
||||
- [Milestones Guide](chapters/milestones.md) - See how each dataset is used in historical achievements
|
||||
- [Student Workflow](student-workflow.md) - Learn the development cycle
|
||||
- [Quick Start](quickstart-guide.md) - Start building in 15 minutes
|
||||
|
||||
**Dataset implementation details**: See `datasets/tinydigits/README.md` and `datasets/tinytalks/README.md` for technical specifications.
|
||||
|
||||
10
site/faq.md
10
site/faq.md
@@ -212,7 +212,7 @@ Milestones are historical ML achievements you recreate with YOUR implementations
|
||||
|
||||
Each milestone proves your framework works by running actual ML experiments.
|
||||
|
||||
**📖 See [Journey Through ML History](chapters/milestones.html)** for details.
|
||||
**📖 See [Journey Through ML History](chapters/milestones.md)** for details.
|
||||
|
||||
### Are the checkpoints required?
|
||||
|
||||
@@ -228,7 +228,7 @@ Each milestone proves your framework works by running actual ML experiments.
|
||||
- Helpful for self-assessment
|
||||
- Use `tito checkpoint status` to view progress
|
||||
|
||||
**📖 See [Student Workflow](student-workflow.html)** for the core development cycle.
|
||||
**📖 See [Student Workflow](student-workflow.md)** for the core development cycle.
|
||||
|
||||
---
|
||||
|
||||
@@ -255,7 +255,7 @@ cd modules/01_tensor
|
||||
jupyter lab tensor_dev.py
|
||||
```
|
||||
|
||||
**📖 See [Quick Start Guide](quickstart-guide.html)** for detailed setup.
|
||||
**📖 See [Quick Start Guide](quickstart-guide.md)** for detailed setup.
|
||||
|
||||
### What's the typical workflow?
|
||||
|
||||
@@ -272,7 +272,7 @@ cd ../../milestones/01_1957_perceptron
|
||||
python rosenblatt_forward.py # Uses YOUR implementation!
|
||||
```
|
||||
|
||||
**📖 See [Student Workflow](student-workflow.html)** for complete details.
|
||||
**📖 See [Student Workflow](student-workflow.md)** for complete details.
|
||||
|
||||
### Can I use this in my classroom?
|
||||
|
||||
@@ -283,7 +283,7 @@ python rosenblatt_forward.py # Uses YOUR implementation!
|
||||
- NBGrader integration coming soon for automated grading
|
||||
- Instructor tooling under development
|
||||
|
||||
**📖 See [Classroom Use Guide](usage-paths/classroom-use.html)** for details.
|
||||
**📖 See [Classroom Use Guide](usage-paths/classroom-use.md)** for details.
|
||||
|
||||
### How do I get help?
|
||||
|
||||
|
||||
@@ -342,7 +342,7 @@ graph LR
|
||||
|
||||
**The essential three-step cycle**: Edit → Export → Validate
|
||||
|
||||
**📖 See [Student Workflow](student-workflow.html)** for detailed workflow guide.
|
||||
**📖 See [Student Workflow](student-workflow.md)** for detailed workflow guide.
|
||||
|
||||
---
|
||||
|
||||
@@ -382,7 +382,7 @@ flowchart TB
|
||||
|
||||
**Strategy**: Start small (shipped datasets), iterate fast, then validate on benchmarks (downloaded datasets).
|
||||
|
||||
**📖 See [Datasets Guide](datasets.html)** for complete dataset documentation.
|
||||
**📖 See [Datasets Guide](datasets.md)** for complete dataset documentation.
|
||||
|
||||
---
|
||||
|
||||
@@ -484,8 +484,8 @@ xychart-beta
|
||||
|
||||
## Related Pages
|
||||
|
||||
- **📖 [Introduction](intro.html)** - What is TinyTorch and why build from scratch
|
||||
- **📖 [Student Workflow](student-workflow.html)** - The essential edit → export → validate cycle
|
||||
- **📖 [Three-Tier Structure](chapters/00-introduction.html)** - Detailed tier breakdown
|
||||
- **📖 [Milestones](chapters/milestones.html)** - Journey through ML history
|
||||
- **📖 [FAQ](faq.html)** - Common questions answered
|
||||
- **📖 [Introduction](intro.md)** - What is TinyTorch and why build from scratch
|
||||
- **📖 [Student Workflow](student-workflow.md)** - The essential edit → export → validate cycle
|
||||
- **📖 [Three-Tier Structure](chapters/00-introduction.md)** - Detailed tier breakdown
|
||||
- **📖 [Milestones](chapters/milestones.md)** - Journey through ML history
|
||||
- **📖 [FAQ](faq.md)** - Common questions answered
|
||||
|
||||
@@ -11,7 +11,7 @@
|
||||
|
||||
TinyTorch follows a simple three-step cycle: **Edit modules → Export to package → Validate with milestones**
|
||||
|
||||
**📖 See [Student Workflow](student-workflow.html)** for the complete development cycle, best practices, and troubleshooting.
|
||||
See [Student Workflow](student-workflow.md) for the complete development cycle, best practices, and troubleshooting.
|
||||
|
||||
## Understanding Modules vs Checkpoints vs Milestones
|
||||
|
||||
@@ -35,7 +35,7 @@ TinyTorch follows a simple three-step cycle: **Edit modules → Export to packag
|
||||
- Tracks capability mastery
|
||||
- Not required for the core workflow
|
||||
|
||||
**📖 See [Journey Through ML History](chapters/milestones.html)** for milestone details.
|
||||
See [Journey Through ML History](chapters/milestones.md) for milestone details.
|
||||
|
||||
</div>
|
||||
|
||||
@@ -43,7 +43,7 @@ TinyTorch follows a simple three-step cycle: **Edit modules → Export to packag
|
||||
|
||||
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.html#three-tier-learning-pathway-build-complete-ml-systems)** for complete tier breakdown, detailed module descriptions, time estimates, and learning outcomes.
|
||||
See [Three-Tier Learning Structure](chapters/00-introduction.md) for complete tier breakdown, detailed module descriptions, time estimates, and learning outcomes.
|
||||
|
||||
## Module Progression Checklist
|
||||
|
||||
@@ -70,7 +70,7 @@ Track your journey through the 20 modules:
|
||||
- [ ] **Module 19**: Benchmarking - MLPerf-style comparison
|
||||
- [ ] **Module 20**: Competition - Capstone challenge
|
||||
|
||||
**📖 See [Quick Start Guide](quickstart-guide.html)** for immediate hands-on experience with your first module.
|
||||
**📖 See [Quick Start Guide](quickstart-guide.md)** for immediate hands-on experience with your first module.
|
||||
|
||||
## Optional: Checkpoint System
|
||||
|
||||
@@ -82,7 +82,7 @@ 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.html)** for checkpoint commands.
|
||||
**📖 See [Essential Commands](tito-essentials.md)** for checkpoint commands.
|
||||
|
||||
---
|
||||
|
||||
@@ -149,6 +149,6 @@ python 01_rosenblatt_forward.py # Uses YOUR implementation!
|
||||
|
||||
**Optional**: Use `tito checkpoint status` to see capability tracking
|
||||
|
||||
**📖 See [Student Workflow](student-workflow.html)** for the complete development cycle.
|
||||
**📖 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.
|
||||
1
site/modules/01_tensor_ABOUT.md
Symbolic link
1
site/modules/01_tensor_ABOUT.md
Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/01_tensor/ABOUT.md
|
||||
1
site/modules/02_activations_ABOUT.md
Symbolic link
1
site/modules/02_activations_ABOUT.md
Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/02_activations/ABOUT.md
|
||||
1
site/modules/03_layers_ABOUT.md
Symbolic link
1
site/modules/03_layers_ABOUT.md
Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/03_layers/ABOUT.md
|
||||
1
site/modules/04_losses_ABOUT.md
Symbolic link
1
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Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/04_losses/ABOUT.md
|
||||
1
site/modules/05_autograd_ABOUT.md
Symbolic link
1
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Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/05_autograd/ABOUT.md
|
||||
1
site/modules/06_optimizers_ABOUT.md
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1
site/modules/06_optimizers_ABOUT.md
Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/06_optimizers/ABOUT.md
|
||||
1
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1
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Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/07_training/ABOUT.md
|
||||
1
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1
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Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/08_dataloader/ABOUT.md
|
||||
1
site/modules/09_spatial_ABOUT.md
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1
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Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/09_spatial/ABOUT.md
|
||||
1
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1
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Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/10_tokenization/ABOUT.md
|
||||
1
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1
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Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/11_embeddings/ABOUT.md
|
||||
1
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1
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Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/12_attention/ABOUT.md
|
||||
1
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1
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@@ -0,0 +1 @@
|
||||
../../modules/13_transformers/ABOUT.md
|
||||
1
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1
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@@ -0,0 +1 @@
|
||||
../../modules/14_profiling/ABOUT.md
|
||||
1
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1
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Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/15_quantization/ABOUT.md
|
||||
1
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1
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@@ -0,0 +1 @@
|
||||
../../modules/16_compression/ABOUT.md
|
||||
1
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1
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@@ -0,0 +1 @@
|
||||
../../modules/17_memoization/ABOUT.md
|
||||
1
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@@ -0,0 +1 @@
|
||||
../../modules/18_acceleration/ABOUT.md
|
||||
1
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@@ -0,0 +1 @@
|
||||
../../modules/19_benchmarking/ABOUT.md
|
||||
1
site/modules/20_capstone_ABOUT.md
Symbolic link
1
site/modules/20_capstone_ABOUT.md
Symbolic link
@@ -0,0 +1 @@
|
||||
../../modules/20_capstone/ABOUT.md
|
||||
@@ -7,7 +7,7 @@
|
||||
|
||||
**Purpose**: Get hands-on experience building ML systems in 15 minutes. Complete setup verification and build your first neural network component from scratch.
|
||||
|
||||
## ⚡ 2-Minute Setup
|
||||
## 2-Minute Setup
|
||||
|
||||
Let's get you ready to build ML systems:
|
||||
|
||||
@@ -27,12 +27,12 @@ source activate.sh
|
||||
```
|
||||
|
||||
**What this does:**
|
||||
- ✅ Creates optimized virtual environment (arm64 on Apple Silicon)
|
||||
- ✅ Installs all dependencies (NumPy, Jupyter, Rich, PyTorch for validation)
|
||||
- ✅ Configures TinyTorch in development mode
|
||||
- ✅ Verifies installation
|
||||
- Creates optimized virtual environment (arm64 on Apple Silicon)
|
||||
- Installs all dependencies (NumPy, Jupyter, Rich, PyTorch for validation)
|
||||
- Configures TinyTorch in development mode
|
||||
- Verifies installation
|
||||
|
||||
**📖 See [Essential Commands](tito-essentials.html)** for detailed workflow and troubleshooting.
|
||||
See [Essential Commands](tito-essentials.md) for detailed workflow and troubleshooting.
|
||||
|
||||
</div>
|
||||
|
||||
@@ -44,13 +44,13 @@ source activate.sh
|
||||
tito system doctor
|
||||
```
|
||||
|
||||
You should see all green checkmarks! This confirms your environment is ready for hands-on ML systems building.
|
||||
You should see all green checkmarks. This confirms your environment is ready for hands-on ML systems building.
|
||||
|
||||
**📖 See [Essential Commands](tito-essentials.html)** for verification commands and troubleshooting.
|
||||
See [Essential Commands](tito-essentials.md) for verification commands and troubleshooting.
|
||||
|
||||
</div>
|
||||
|
||||
## 🏗️ 15-Minute First Module Walkthrough
|
||||
## 15-Minute First Module Walkthrough
|
||||
|
||||
Let's build your first neural network component following the **TinyTorch workflow**:
|
||||
|
||||
@@ -58,17 +58,17 @@ Let's build your first neural network component following the **TinyTorch workfl
|
||||
1. Edit modules → 2. Export to package → 3. Validate with milestones
|
||||
```
|
||||
|
||||
**📖 See [Student Workflow](student-workflow.html)** for the complete development cycle.
|
||||
See [Student Workflow](student-workflow.md) for the complete development cycle.
|
||||
|
||||
### Module 01: Tensor Foundations
|
||||
|
||||
<div style="background: #fffbeb; padding: 1.5rem; border-radius: 0.5rem; border-left: 4px solid #f59e0b; margin: 1.5rem 0;">
|
||||
|
||||
**🎯 Learning Goal:** Build N-dimensional arrays - the foundation of all neural networks
|
||||
**Learning Goal:** Build N-dimensional arrays - the foundation of all neural networks
|
||||
|
||||
**⏱️ Time:** 15 minutes
|
||||
**Time:** 15 minutes
|
||||
|
||||
**💻 Action:** Start with Module 01 to build tensor operations from scratch.
|
||||
**Action:** Start with Module 01 to build tensor operations from scratch.
|
||||
|
||||
```bash
|
||||
# Step 1: Edit the module source
|
||||
@@ -91,9 +91,9 @@ tito module complete 01
|
||||
|
||||
This makes your implementation importable: `from tinytorch import Tensor`
|
||||
|
||||
**📖 See [Student Workflow](student-workflow.html)** for the complete edit → export → validate cycle.
|
||||
See [Student Workflow](student-workflow.md) for the complete edit → export → validate cycle.
|
||||
|
||||
**✅ Achievement Unlocked:** Foundation capability - "Can I create and manipulate the building blocks of ML?"
|
||||
**Achievement Unlocked:** Foundation capability - "Can I create and manipulate the building blocks of ML?"
|
||||
|
||||
</div>
|
||||
|
||||
@@ -101,11 +101,11 @@ This makes your implementation importable: `from tinytorch import Tensor`
|
||||
|
||||
<div style="background: #fdf2f8; padding: 1.5rem; border-radius: 0.5rem; border-left: 4px solid #ec4899; margin: 1.5rem 0;">
|
||||
|
||||
**🎯 Learning Goal:** Add nonlinearity - the key to neural network intelligence
|
||||
**Learning Goal:** Add nonlinearity - the key to neural network intelligence
|
||||
|
||||
**⏱️ Time:** 10 minutes
|
||||
**Time:** 10 minutes
|
||||
|
||||
**💻 Action:** Continue with Module 02 to add activation functions.
|
||||
**Action:** Continue with Module 02 to add activation functions.
|
||||
|
||||
```bash
|
||||
# Step 1: Edit the module
|
||||
@@ -126,13 +126,13 @@ You'll implement essential activation functions:
|
||||
tito module complete 02
|
||||
```
|
||||
|
||||
**📖 See [Student Workflow](student-workflow.html)** for the complete edit → export → validate cycle.
|
||||
See [Student Workflow](student-workflow.md) for the complete edit → export → validate cycle.
|
||||
|
||||
**✅ Achievement Unlocked:** Intelligence capability - "Can I add nonlinearity to enable learning?"
|
||||
**Achievement Unlocked:** Intelligence capability - "Can I add nonlinearity to enable learning?"
|
||||
|
||||
</div>
|
||||
|
||||
## 📊 Track Your Progress
|
||||
## Track Your Progress
|
||||
|
||||
After completing your first modules:
|
||||
|
||||
@@ -146,73 +146,73 @@ tito checkpoint status # View your completion tracking
|
||||
|
||||
This is helpful for self-assessment but not required for the core workflow.
|
||||
|
||||
**📖 See [Student Workflow](student-workflow.html)** for the essential edit → export → validate cycle, and [Track Your Progress](learning-progress.html)** for detailed capability tracking.
|
||||
See [Student Workflow](student-workflow.md) for the essential edit → export → validate cycle, and [Track Your Progress](learning-progress.md) for detailed capability tracking.
|
||||
|
||||
</div>
|
||||
|
||||
## 🏆 Validate with Historical Milestones
|
||||
## Validate with Historical Milestones
|
||||
|
||||
After exporting your modules, **prove what you've built** by running milestone scripts:
|
||||
|
||||
<div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 2rem; border-radius: 0.5rem; margin: 1.5rem 0; color: white;">
|
||||
|
||||
**After Module 07**: Build **Rosenblatt's 1957 Perceptron** - the first trainable neural network
|
||||
**After Module 07**: Solve the **1969 XOR Crisis** with multi-layer networks
|
||||
**After Module 08**: Achieve **95%+ accuracy on MNIST** with 1986 backpropagation
|
||||
**After Module 09**: Hit **75%+ on CIFAR-10** with 1998 CNNs
|
||||
**After Module 13**: Generate text with **2017 Transformers**
|
||||
**After Module 07**: Build **Rosenblatt's 1957 Perceptron** - the first trainable neural network
|
||||
**After Module 07**: Solve the **1969 XOR Crisis** with multi-layer networks
|
||||
**After Module 08**: Achieve **95%+ accuracy on MNIST** with 1986 backpropagation
|
||||
**After Module 09**: Hit **75%+ on CIFAR-10** with 1998 CNNs
|
||||
**After Module 13**: Generate text with **2017 Transformers**
|
||||
**After Module 18**: Optimize for production with **2018 MLPerf**
|
||||
|
||||
**📖 See [Journey Through ML History](chapters/milestones.html)** for complete timeline, requirements, and expected results.
|
||||
See [Journey Through ML History](chapters/milestones.md) for complete timeline, requirements, and expected results.
|
||||
|
||||
</div>
|
||||
|
||||
**The Workflow**: Edit modules → Export with `tito module complete N` → Run milestone scripts to validate
|
||||
|
||||
**📖 See [Student Workflow](student-workflow.html)** for the complete cycle.
|
||||
See [Student Workflow](student-workflow.md) for the complete cycle.
|
||||
|
||||
## 🎯 What You Just Accomplished
|
||||
## What You Just Accomplished
|
||||
|
||||
In 15 minutes, you've:
|
||||
|
||||
<div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 1rem; margin: 2rem 0;">
|
||||
|
||||
<div style="background: #e6fffa; padding: 1rem; border-radius: 0.5rem; border-left: 3px solid #26d0ce;">
|
||||
<h4 style="margin: 0 0 0.5rem 0; color: #0d9488;">🔧 Setup Complete</h4>
|
||||
<h4 style="margin: 0 0 0.5rem 0; color: #0d9488;">Setup Complete</h4>
|
||||
<p style="margin: 0; font-size: 0.9rem;">Installed TinyTorch and verified your environment</p>
|
||||
</div>
|
||||
|
||||
<div style="background: #f0f9ff; padding: 1rem; border-radius: 0.5rem; border-left: 3px solid #3b82f6;">
|
||||
<h4 style="margin: 0 0 0.5rem 0; color: #1d4ed8;">🧱 Created Foundation</h4>
|
||||
<h4 style="margin: 0 0 0.5rem 0; color: #1d4ed8;">Created Foundation</h4>
|
||||
<p style="margin: 0; font-size: 0.9rem;">Implemented core tensor operations from scratch</p>
|
||||
</div>
|
||||
|
||||
<div style="background: #fefce8; padding: 1rem; border-radius: 0.5rem; border-left: 3px solid #eab308;">
|
||||
<h4 style="margin: 0 0 0.5rem 0; color: #a16207;">🏆 First Capability</h4>
|
||||
<h4 style="margin: 0 0 0.5rem 0; color: #a16207;">First Capability</h4>
|
||||
<p style="margin: 0; font-size: 0.9rem;">Earned your first ML systems capability checkpoint</p>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
|
||||
## 🚀 Your Next Steps
|
||||
## Your Next Steps
|
||||
|
||||
<div style="background: #f8f9fa; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0;">
|
||||
|
||||
### Immediate Next Actions (Choose One):
|
||||
|
||||
**🔥 Continue Building (Recommended):** Begin Module 03 to add layers to your network.
|
||||
**Continue Building (Recommended):** Begin Module 03 to add layers to your network.
|
||||
|
||||
**📚 Master the Workflow:**
|
||||
- **📖 See [Student Workflow](student-workflow.html)** for the complete edit → export → validate cycle
|
||||
- **📖 See [Essential Commands](tito-essentials.html)** for complete TITO command reference
|
||||
- **📖 See [Track Your Progress](learning-progress.html)** for the full learning path
|
||||
**Master the Workflow:**
|
||||
- See [Student Workflow](student-workflow.md) for the complete edit → export → validate cycle
|
||||
- See [Essential Commands](tito-essentials.md) for complete TITO command reference
|
||||
- See [Track Your Progress](learning-progress.md) for the full learning path
|
||||
|
||||
**🎓 For Instructors:**
|
||||
- **📖 See [Classroom Setup Guide](usage-paths/classroom-use.html)** for NBGrader integration (coming soon)
|
||||
**For Instructors:**
|
||||
- See [Classroom Setup Guide](usage-paths/classroom-use.md) for NBGrader integration (coming soon)
|
||||
|
||||
</div>
|
||||
|
||||
## 💡 Pro Tips for Continued Success
|
||||
## Pro Tips for Continued Success
|
||||
|
||||
<div style="background: #fff5f5; padding: 1.5rem; border: 1px solid #fed7d7; border-radius: 0.5rem; margin: 1rem 0;">
|
||||
|
||||
@@ -221,11 +221,11 @@ In 15 minutes, you've:
|
||||
2. Export with `tito module complete N`
|
||||
3. Validate by running milestone scripts
|
||||
|
||||
**📖 See [Student Workflow](student-workflow.html)** for detailed workflow guide and best practices.
|
||||
See [Student Workflow](student-workflow.md) for detailed workflow guide and best practices.
|
||||
|
||||
</div>
|
||||
|
||||
## 🌟 You're Now a TinyTorch Builder!
|
||||
## You're Now a TinyTorch Builder
|
||||
|
||||
<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;">Ready to Build Production ML Systems</h3>
|
||||
@@ -238,4 +238,4 @@ In 15 minutes, you've:
|
||||
|
||||
**What makes TinyTorch different:** You're not just learning *about* neural networks—you're building them from fundamental mathematical operations. Every line of code you write builds toward complete ML systems mastery.
|
||||
|
||||
**Next milestone:** After Module 08, you'll train real neural networks on actual datasets using 100% your own code!
|
||||
**Next milestone:** After Module 08, you'll train real neural networks on actual datasets using 100% your own code!
|
||||
|
||||
0
site/references.bib
Normal file
0
site/references.bib
Normal file
@@ -1,4 +1,4 @@
|
||||
# 📚 Additional Learning Resources
|
||||
# Additional Learning Resources
|
||||
|
||||
<div style="background: #f8f9fa; border: 1px solid #dee2e6; padding: 2rem; border-radius: 0.5rem; text-align: center; margin: 2rem 0;">
|
||||
<h2 style="margin: 0 0 1rem 0; color: #495057;">Complement Your TinyTorch Journey</h2>
|
||||
@@ -8,14 +8,13 @@
|
||||
While TinyTorch teaches you to build complete ML systems from scratch, these resources provide broader context, alternative perspectives, and production tools.
|
||||
|
||||
**TinyTorch Learning Resources:**
|
||||
- **📖 See [Track Your Progress](learning-progress.html)** for monitoring capability development and learning progression
|
||||
- **📖 See [Progress Tracking](checkpoint-system.html)** for technical details on capability testing
|
||||
- **📖 See [Testing Guide](testing-framework.html)** for comprehensive testing methodology
|
||||
- **📖 See [Achievement Showcase](leaderboard.html)** for portfolio development and career readiness
|
||||
- See [Track Your Progress](learning-progress.md) for monitoring capability development and learning progression
|
||||
- See [Progress Tracking](checkpoint-system.md) for technical details on capability testing
|
||||
- See [Testing Guide](testing-framework.md) for comprehensive testing methodology
|
||||
|
||||
---
|
||||
|
||||
## 🎓 Academic Courses
|
||||
## Academic Courses
|
||||
|
||||
### Machine Learning Systems
|
||||
- **[CS 329S: Machine Learning Systems Design](https://stanford-cs329s.github.io/)** (Stanford)
|
||||
@@ -36,7 +35,7 @@ While TinyTorch teaches you to build complete ML systems from scratch, these res
|
||||
|
||||
---
|
||||
|
||||
## 📖 Recommended Books
|
||||
## Recommended Books
|
||||
|
||||
### Systems & Engineering
|
||||
- **[Machine Learning Systems](https://mlsysbook.ai)** by Prof. Vijay Janapa Reddi (Harvard)
|
||||
@@ -57,7 +56,7 @@ While TinyTorch teaches you to build complete ML systems from scratch, these res
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ Alternative Implementations
|
||||
## Alternative Implementations
|
||||
|
||||
**Different approaches to building ML systems from scratch - see how others tackle the same challenge:**
|
||||
|
||||
@@ -76,31 +75,31 @@ While TinyTorch teaches you to build complete ML systems from scratch, these res
|
||||
|
||||
---
|
||||
|
||||
## 🏭 Production Internals
|
||||
## Production Internals
|
||||
|
||||
### Framework Deep Dives
|
||||
- **[PyTorch Internals](http://blog.ezyang.com/2019/05/pytorch-internals/)** by Edward Yang
|
||||
*How PyTorch actually works under the hood - a great read as see what you built in TinyTorch corresponds to the real PyTorch*
|
||||
|
||||
- **[PyTorch Documentation: Extending PyTorch](https://pytorch.org/docs/stable/notes/extending.html)**
|
||||
- **[PyTorch Documentation: Extending PyTorch](https://pytorch.org/docs/stable/notes/extending.md)**
|
||||
*Custom operators and autograd functions - apply your TinyTorch knowledge*
|
||||
|
||||
---
|
||||
|
||||
*Building ML systems from scratch gives you the implementation foundation most ML engineers lack. These resources help you apply that knowledge to broader systems and production environments.*
|
||||
|
||||
## 🚀 Ready to Begin Your Journey?
|
||||
## Ready to Begin Your Journey?
|
||||
|
||||
**Start with the fundamentals and build your way up.**
|
||||
|
||||
**📖 See [Essential Commands](tito-essentials.html)** for complete TITO command reference.
|
||||
See [Essential Commands](tito-essentials.md) for complete TITO command reference.
|
||||
|
||||
**Your Next Steps:**
|
||||
1. **📖 See [Quick Start Guide](quickstart-guide.html)** for 15-minute hands-on experience
|
||||
2. **📖 See [Track Your Progress](learning-progress.html)** for understanding capability development
|
||||
3. **📖 See [Course Introduction](chapters/00-introduction.html)** for deep dive into course philosophy
|
||||
1. See [Quick Start Guide](quickstart-guide.md) for 15-minute hands-on experience
|
||||
2. See [Track Your Progress](learning-progress.md) for understanding capability development
|
||||
3. See [Course Introduction](chapters/00-introduction.md) for deep dive into course philosophy
|
||||
|
||||
<div style="background: #f8f9fa; border: 1px solid #dee2e6; padding: 1.5rem; border-radius: 0.5rem; margin: 2rem 0; text-align: center;">
|
||||
<h4 style="margin: 0 0 1rem 0; color: #495057;">🎯 Transform from Framework User to Systems Engineer</h4>
|
||||
<h4 style="margin: 0 0 1rem 0; color: #495057;">Transform from Framework User to Systems Engineer</h4>
|
||||
<p style="margin: 0; color: #6c757d;">These external resources complement the hands-on systems building you'll do in TinyTorch</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -70,14 +70,14 @@ See [Milestones Guide](chapters/milestones.md) for the full progression.
|
||||
|
||||
TinyTorch has 20 modules organized in three tiers:
|
||||
|
||||
### 🏗️ Foundation (Modules 01-07)
|
||||
### Foundation (Modules 01-07)
|
||||
Core ML infrastructure - tensors, autograd, training loops
|
||||
|
||||
**Milestones unlocked:**
|
||||
- M01: Perceptron (after Module 07)
|
||||
- M02: XOR Crisis (after Module 07)
|
||||
|
||||
### 🏛️ Architecture (Modules 08-13)
|
||||
### Architecture (Modules 08-13)
|
||||
Neural network architectures - data loading, CNNs, transformers
|
||||
|
||||
**Milestones unlocked:**
|
||||
@@ -85,12 +85,15 @@ Neural network architectures - data loading, CNNs, transformers
|
||||
- M04: CNNs (after Module 09)
|
||||
- M05: Transformers (after Module 13)
|
||||
|
||||
### ⚡ Optimization (Modules 14-20)
|
||||
Production optimization - profiling, quantization, benchmarking, capstone
|
||||
### Optimization (Modules 14-19)
|
||||
Production optimization - profiling, quantization, benchmarking
|
||||
|
||||
**Milestones unlocked:**
|
||||
- M06: MLPerf (after Module 18)
|
||||
|
||||
### Capstone Competition (Module 20)
|
||||
Apply all optimizations in the MLPerf® Edu Competition
|
||||
|
||||
## Typical Development Session
|
||||
|
||||
Here's what a typical session looks like:
|
||||
|
||||
@@ -1,17 +1,17 @@
|
||||
# 🧪 Testing Framework
|
||||
# Testing Framework
|
||||
|
||||
```{admonition} Test-Driven ML Engineering
|
||||
:class: tip
|
||||
TinyTorch's testing framework ensures your implementations are not just educational, but production-ready and reliable.
|
||||
```
|
||||
|
||||
## 🎯 Testing Philosophy: Verify Understanding Through Implementation
|
||||
## Testing Philosophy: Verify Understanding Through Implementation
|
||||
|
||||
TinyTorch testing goes beyond checking syntax - it validates that you understand ML systems engineering through working implementations.
|
||||
|
||||
## ⚡ Quick Start: Validate Your Implementation
|
||||
## Quick Start: Validate Your Implementation
|
||||
|
||||
### 🚀 Run Everything (Recommended)
|
||||
### Run Everything (Recommended)
|
||||
```bash
|
||||
# Complete validation suite
|
||||
tito test --comprehensive
|
||||
@@ -23,7 +23,7 @@ tito test --comprehensive
|
||||
# ✅ Overall TinyTorch Health: 100.0%
|
||||
```
|
||||
|
||||
### 🎯 Target-Specific Testing
|
||||
### Target-Specific Testing
|
||||
```bash
|
||||
# Test what you just built
|
||||
tito module complete 02_tensor && tito checkpoint test 01
|
||||
@@ -35,9 +35,9 @@ tito test --module attention --verbose
|
||||
tito test --performance --module training
|
||||
```
|
||||
|
||||
## 🔬 Testing Levels: From Components to Systems
|
||||
## Testing Levels: From Components to Systems
|
||||
|
||||
### 1. 🧩 Module-Level Testing
|
||||
### 1. Module-Level Testing
|
||||
**Goal**: Verify individual components work correctly in isolation
|
||||
|
||||
```bash
|
||||
@@ -91,7 +91,7 @@ tito checkpoint test 13 # "Can I build attention mechanisms?"
|
||||
tito checkpoint validate --from 00 --to 15
|
||||
```
|
||||
|
||||
**📖 See [Complete Checkpoint System Documentation](checkpoint-system.html)** for technical implementation details.
|
||||
**📖 See [Complete Checkpoint System Documentation](checkpoint-system.md)** for technical implementation details.
|
||||
|
||||
**Key Capability Categories:**
|
||||
- **Foundation (00-03)**: Building blocks of neural networks
|
||||
@@ -374,9 +374,9 @@ tito checkpoint status
|
||||
```
|
||||
|
||||
**Testing Integration with Your Learning Path:**
|
||||
- **📖 See [Track Your Progress](learning-progress.html)** for how testing fits into capability development
|
||||
- **📖 See [Track Capabilities](checkpoint-system.html)** for automated testing and progress validation
|
||||
- **📖 See [Showcase Achievements](leaderboard.html)** for how testing validates the skills you can claim
|
||||
- **📖 See [Track Your Progress](learning-progress.md)** for how testing fits into capability development
|
||||
- **📖 See [Track Capabilities](checkpoint-system.md)** for automated testing and progress validation
|
||||
- **📖 See [Historical Milestones](chapters/milestones.md)** for how testing validates achievements
|
||||
|
||||
<div style="background: #e3f2fd; border: 2px solid #1976d2; padding: 1.5rem; border-radius: 0.5rem; margin: 2rem 0; text-align: center;">
|
||||
<h4 style="margin: 0 0 1rem 0; color: #1565c0;">🎯 Testing Excellence = ML Systems Mastery</h4>
|
||||
|
||||
@@ -13,37 +13,37 @@ TinyTorch follows a simple three-step cycle: **Edit modules → Export to packag
|
||||
|
||||
**The essential command**: `tito module complete MODULE_NUMBER` - exports your code to the TinyTorch package.
|
||||
|
||||
**📖 See [Student Workflow](student-workflow.html)** for the complete development cycle, best practices, and troubleshooting.
|
||||
See [Student Workflow](student-workflow.md) for the complete development cycle, best practices, and troubleshooting.
|
||||
|
||||
This page documents all available TITO commands. The checkpoint system (`tito checkpoint status`) is optional for progress tracking.
|
||||
|
||||
## 🚀 Most Important Commands
|
||||
## Most Important Commands
|
||||
|
||||
The commands you'll use most often:
|
||||
|
||||
<div style="display: grid; grid-template-columns: 1fr; gap: 1rem; margin: 2rem 0;">
|
||||
|
||||
<div style="background: #e3f2fd; padding: 1.5rem; border-radius: 0.5rem; border-left: 4px solid #2196f3;">
|
||||
<h4 style="margin: 0 0 0.5rem 0; color: #1976d2;">📋 Check Your Environment</h4>
|
||||
<h4 style="margin: 0 0 0.5rem 0; color: #1976d2;">Check Your Environment</h4>
|
||||
<code style="background: #263238; color: #ffffff; padding: 0.5rem; border-radius: 0.25rem; display: block; margin: 0.5rem 0;">tito system doctor</code>
|
||||
<p style="margin: 0.5rem 0 0 0; font-size: 0.9rem; color: #64748b;">Verify your setup is ready for development</p>
|
||||
</div>
|
||||
|
||||
<div style="background: #fffbeb; padding: 1.5rem; border-radius: 0.5rem; border-left: 4px solid #f59e0b;">
|
||||
<h4 style="margin: 0 0 0.5rem 0; color: #d97706;">🔨 Export Module to Package (Essential)</h4>
|
||||
<h4 style="margin: 0 0 0.5rem 0; color: #d97706;">Export Module to Package (Essential)</h4>
|
||||
<code style="background: #263238; color: #ffffff; padding: 0.5rem; border-radius: 0.25rem; display: block; margin: 0.5rem 0;">tito module complete 01</code>
|
||||
<p style="margin: 0.5rem 0 0 0; font-size: 0.9rem; color: #64748b;">Export your module to the TinyTorch package - use this after editing modules</p>
|
||||
</div>
|
||||
|
||||
<div style="background: #f0fdf4; padding: 1.5rem; border-radius: 0.5rem; border-left: 4px solid #22c55e;">
|
||||
<h4 style="margin: 0 0 0.5rem 0; color: #15803d;">🎯 Track Your Progress (Optional)</h4>
|
||||
<h4 style="margin: 0 0 0.5rem 0; color: #15803d;">Track Your Progress (Optional)</h4>
|
||||
<code style="background: #263238; color: #ffffff; padding: 0.5rem; border-radius: 0.25rem; display: block; margin: 0.5rem 0;">tito checkpoint status</code>
|
||||
<p style="margin: 0.5rem 0 0 0; font-size: 0.9rem; color: #64748b;">See which capabilities you've mastered (optional capability tracking)</p>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
|
||||
## 🔄 Typical Development Session
|
||||
## Typical Development Session
|
||||
|
||||
Here's what a typical session looks like:
|
||||
|
||||
@@ -73,13 +73,13 @@ python 01_rosenblatt_forward.py # Uses YOUR implementation!
|
||||
tito checkpoint status # See what you've completed
|
||||
```
|
||||
|
||||
**📖 See [Student Workflow](student-workflow.html)** for complete development cycle details.
|
||||
See [Student Workflow](student-workflow.md) for complete development cycle details.
|
||||
|
||||
</div>
|
||||
|
||||
## 📖 Complete Command Reference
|
||||
## Complete Command Reference
|
||||
|
||||
### 🏥 System & Health
|
||||
### System & Health
|
||||
<div style="background: #f8f9fa; padding: 1rem; border-radius: 0.25rem; margin: 1rem 0;">
|
||||
|
||||
**System Check**
|
||||
@@ -96,7 +96,7 @@ tito system info
|
||||
|
||||
</div>
|
||||
|
||||
### 🔨 Module Management
|
||||
### Module Management
|
||||
<div style="background: #f8f9fa; padding: 1rem; border-radius: 0.25rem; margin: 1rem 0;">
|
||||
|
||||
**Export Module to Package (Essential)**
|
||||
@@ -117,7 +117,7 @@ from tinytorch.autograd import backward # YOUR implementation!
|
||||
|
||||
</div>
|
||||
|
||||
### 📊 Progress Tracking (Optional)
|
||||
### Progress Tracking (Optional)
|
||||
<div style="background: #f8f9fa; padding: 1rem; border-radius: 0.25rem; margin: 1rem 0;">
|
||||
|
||||
**Capability Overview**
|
||||
@@ -146,7 +146,7 @@ tito checkpoint test CHECKPOINT_NUMBER
|
||||
|
||||
</div>
|
||||
|
||||
## 👩🏫 Instructor Commands (Coming Soon)
|
||||
## Instructor Commands (Coming Soon)
|
||||
|
||||
<div style="background: #f3e5f5; padding: 1rem; border-radius: 0.25rem; margin: 1rem 0;">
|
||||
|
||||
@@ -154,11 +154,11 @@ TinyTorch includes NBGrader integration for classroom use. Full documentation fo
|
||||
|
||||
**For now, focus on the student workflow**: edit modules → export → validate with milestones.
|
||||
|
||||
*For current instructor capabilities, see [Classroom Use Guide](usage-paths/classroom-use.html)*
|
||||
*For current instructor capabilities, see [Classroom Use Guide](usage-paths/classroom-use.md)*
|
||||
|
||||
</div>
|
||||
|
||||
## 🚨 Troubleshooting Commands
|
||||
## Troubleshooting Commands
|
||||
|
||||
When things go wrong, these commands help:
|
||||
|
||||
@@ -178,7 +178,7 @@ tito checkpoint timeline # Visualize your progress
|
||||
|
||||
</div>
|
||||
|
||||
## 🚀 Ready to Build?
|
||||
## Ready to Build?
|
||||
|
||||
<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;">Start Your TinyTorch Journey</h3>
|
||||
@@ -189,4 +189,4 @@ tito checkpoint timeline # Visualize your progress
|
||||
|
||||
---
|
||||
|
||||
*Master these commands and you'll build ML systems with confidence. Every command is designed to accelerate your learning and keep you focused on what matters: building production-quality ML frameworks from scratch.*
|
||||
*Master these commands and you'll build ML systems with confidence. Every command is designed to accelerate your learning and keep you focused on what matters: building production-quality ML frameworks from scratch.*
|
||||
|
||||
@@ -108,7 +108,7 @@
|
||||
|
||||
The TinyTorch course consists of 20 progressive modules organized into learning stages.
|
||||
|
||||
**📖 See [Complete Course Structure](../chapters/00-introduction.html#course-structure)** for detailed module descriptions, learning objectives, and prerequisites for each module.
|
||||
**📖 See [Complete Course Structure](../chapters/00-introduction.md)** for detailed module descriptions, learning objectives, and prerequisites for each module.
|
||||
|
||||
---
|
||||
|
||||
@@ -198,9 +198,9 @@ tito nbgrader release 01_setup
|
||||
## Instructor Resources
|
||||
|
||||
### Documentation
|
||||
- [Complete Instructor Guide](../instructor-guide.md) - Detailed setup and workflow
|
||||
- [Quick Reference Card](../../NBGrader_Quick_Reference.md) - Essential commands
|
||||
- Module-specific teaching notes in each chapter
|
||||
- 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
|
||||
@@ -216,10 +216,10 @@ tito nbgrader release 01_setup
|
||||
|
||||
## 📞 Next Steps
|
||||
|
||||
1. **📖 Read the [Instructor Guide](../instructor-guide.md)** for complete details
|
||||
2. **🚀 Start with Module 0: [Introduction](../chapters/00-introduction.md)** to see the system overview
|
||||
3. **💻 Set up your environment** following the guide
|
||||
4. **📧 Contact us** for instructor support
|
||||
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
|
||||
|
||||
---
|
||||
|
||||
|
||||
Reference in New Issue
Block a user