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Add visual learning journey with Mermaid diagrams and integrate new pages

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## New Documentation Pages Integrated into Site Navigation

**site/learning-journey-visual.md** - 10 interactive Mermaid diagrams:
1. Complete Learning Flow - Full flowchart through all 20 modules & 6 milestones
2. Module Dependencies - Shows how modules depend on each other
3. Three-Tier Timeline - Visual progression through Foundation/Architecture/Optimization
4. Historical Milestones - Gantt chart showing ML history recreation (1957→2024)
5. Student Learning Paths - Decision tree for different learning approaches
6. Capability Progression - Skill levels unlocked at each tier
7. Workflow Cycle - The edit → export → validate loop
8. Dataset Strategy - When to use shipped vs downloaded datasets
9. Time vs Outcomes Quadrant - Comparing learning path investments
10. Difficulty Curve - Line chart showing module difficulty progression

## Site Navigation Updates (_toc.yml)

**Added to "Using TinyTorch" section:**
- Student Workflow (student-workflow.md) - Essential edit → export → validate cycle
- Datasets Guide (datasets.md) - Complete dataset documentation

**Added to "Course Orientation" section:**
- Visual Learning Map (learning-journey-visual.md) - NEW Mermaid diagram showcase
- FAQ (faq.md) - Comprehensive answers to common questions

## Mermaid Integration

- Mermaid already configured in _config.yml (v10.6.1)
- All diagrams use color coding:
  - Blue: Foundation modules
  - Orange: Critical modules (Autograd, Training)
  - Purple: Advanced architecture modules
  - Green: Milestone achievements
  - Yellow: North Star milestone (CIFAR-10)
  - Red: Capstone

## Benefits

**Visual learners**: Diagrams show the complete learning journey at a glance
**Navigation**: All new pages now appear in site sidebar
**Discoverability**: FAQ answers "Why TinyTorch vs alternatives"
**Dataset clarity**: Students understand shipped vs downloaded data strategy
**Journey visualization**: See the path from tensors to transformers [Claude Code](https://claude.com/claude-code)
This commit is contained in:
Vijay Janapa Reddi
2025-11-11 22:13:31 -05:00
parent 94b25debbd
commit b96f988bb2
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site/_toc.yml
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@@ -17,8 +17,12 @@ parts:
chapters:
- file: tito-essentials
title: "Essential Commands"
- file: student-workflow
title: "Student Workflow"
- file: learning-progress
title: "Track Your Progress"
- file: datasets
title: "Datasets Guide"
- caption: 🧭 Course Orientation
chapters:
@@ -26,8 +30,12 @@ parts:
title: "Course Structure"
- file: chapters/learning-journey
title: "Learning Journey"
- file: learning-journey-visual
title: "Visual Learning Map"
- file: chapters/milestones
title: "Historical Milestones"
- file: faq
title: "FAQ"
- caption: 🏗️ Foundation Tier (01-07)
chapters:

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# Visual Learning Journey
<div style="background: #f8f9fa; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0; text-align: center;">
<h2 style="margin: 0 0 1rem 0; color: #495057;">The TinyTorch Learning Journey</h2>
<p style="margin: 0; font-size: 1.1rem; color: #6c757d;">Visual roadmap from tensors to transformers</p>
</div>
**Purpose**: Visualize the learning progression, module dependencies, and milestone achievements in TinyTorch.
---
## The Complete Learning Flow
```{mermaid}
%%{init: {'theme':'base', 'themeVariables': { 'primaryColor':'#e3f2fd','primaryTextColor':'#1976d2','primaryBorderColor':'#2196f3','lineColor':'#2196f3','secondaryColor':'#fff3e0','tertiaryColor':'#f3e5f5'}}}%%
flowchart TB
Start([Start: Setup Environment]) --> M01[Module 01: Tensor]
subgraph Foundation["🏗️ Foundation Tier (Modules 01-07)"]
M01 --> M02[Module 02: Activations]
M02 --> M03[Module 03: Layers]
M03 --> M04[Module 04: Losses]
M04 --> M05[Module 05: Autograd]
M05 --> M06[Module 06: Optimizers]
M06 --> M07[Module 07: Training]
end
M07 --> MS01{{"🏆 M01: 1957 Perceptron"}}
M07 --> MS02{{"🏆 M02: 1969 XOR"}}
MS02 --> M08[Module 08: DataLoader]
M08 --> MS03{{"🏆 M03: 1986 MLP<br/>95%+ MNIST"}}
subgraph Architecture["🏛️ Architecture Tier (Modules 08-13)"]
M08 --> M09[Module 09: Spatial/CNNs]
M08 --> M10[Module 10: Tokenization]
M09 --> MS04{{"🏆 M04: 1998 CNN<br/>75%+ CIFAR-10"}}
M10 --> M11[Module 11: Embeddings]
M11 --> M12[Module 12: Attention]
M12 --> M13[Module 13: Transformers]
end
M13 --> MS05{{"🏆 M05: 2017 Transformers<br/>Text Generation"}}
subgraph Optimization["⚡ Optimization Tier (Modules 14-20)"]
MS05 --> M14[Module 14: Profiling]
M14 --> M15[Module 15: Quantization]
M14 --> M16[Module 16: Compression]
M14 --> M17[Module 17: Memoization]
M15 --> M18[Module 18: Acceleration]
M16 --> M18
M17 --> M18
M18 --> M19[Module 19: Benchmarking]
M19 --> M20[Module 20: Competition]
end
M20 --> MS06{{"🏆 M06: 2024 MLPerf<br/>Production System"}}
MS06 --> Complete([🎓 Complete!<br/>ML Systems Engineer])
style M01 fill:#e3f2fd,stroke:#2196f3,stroke-width:2px
style M05 fill:#fff3e0,stroke:#f57c00,stroke-width:3px
style M07 fill:#fff3e0,stroke:#f57c00,stroke-width:3px
style M09 fill:#f3e5f5,stroke:#9c27b0,stroke-width:2px
style M13 fill:#f3e5f5,stroke:#9c27b0,stroke-width:3px
style M20 fill:#fce4ec,stroke:#e91e63,stroke-width:3px
style MS01 fill:#c8e6c9,stroke:#4caf50,stroke-width:2px
style MS02 fill:#c8e6c9,stroke:#4caf50,stroke-width:2px
style MS03 fill:#c8e6c9,stroke:#4caf50,stroke-width:2px
style MS04 fill:#fff9c4,stroke:#fbc02d,stroke-width:3px
style MS05 fill:#c8e6c9,stroke:#4caf50,stroke-width:2px
style MS06 fill:#ffccbc,stroke:#ff5722,stroke-width:3px
style Complete fill:#b2dfdb,stroke:#009688,stroke-width:4px
```
**Legend:**
- 🟦 Blue: Foundation modules
- 🟧 Orange highlights: Critical modules (Autograd, Training)
- 🟪 Purple: Advanced architecture modules
- 🟩 Green: Milestone achievements
- 🟨 Yellow: North Star milestone (CIFAR-10)
- 🟥 Red: Capstone
---
## Module Dependencies
```{mermaid}
%%{init: {'theme':'base', 'themeVariables': { 'primaryColor':'#e8f5e9','primaryTextColor':'#2e7d32','primaryBorderColor':'#4caf50'}}}%%
graph LR
subgraph Core["Core Foundation"]
T[01 Tensor] --> A[02 Activations]
T --> L[03 Layers]
T --> Lo[04 Losses]
T --> D[08 DataLoader]
end
subgraph Training["Training Engine"]
T -.enhances.-> AG[05 Autograd]
AG --> O[06 Optimizers]
L --> O
O --> TR[07 Training]
Lo --> TR
end
subgraph Vision["Computer Vision"]
T --> S[09 Spatial]
A --> S
L --> S
AG --> S
end
subgraph Language["NLP Pipeline"]
T --> TK[10 Tokenization]
TK --> E[11 Embeddings]
T --> E
E --> AT[12 Attention]
L --> AT
AG --> AT
AT --> TF[13 Transformers]
A --> TF
E --> TF
end
subgraph Opt["Optimization"]
P[14 Profiling] --> Q[15 Quantization]
P --> C[16 Compression]
P --> M[17 Memoization]
Q --> AC[18 Acceleration]
C --> AC
M --> AC
AC --> B[19 Benchmarking]
B --> CP[20 Competition]
end
TR --> S
TR --> TF
S -.optimized by.-> Opt
TF -.optimized by.-> Opt
style T fill:#ffeb3b,stroke:#f57c00,stroke-width:4px
style AG fill:#ff9800,stroke:#e65100,stroke-width:4px
style TR fill:#ff9800,stroke:#e65100,stroke-width:4px
style S fill:#9c27b0,stroke:#4a148c,stroke-width:3px
style TF fill:#9c27b0,stroke:#4a148c,stroke-width:3px
style CP fill:#f44336,stroke:#b71c1c,stroke-width:3px
```
**Key Dependencies:**
- **Tensor (Module 01)**: Foundation for everything - all modules depend on it
- **Autograd (Module 05)**: Enhances Tensor, enables all learning
- **Training (Module 07)**: Orchestrates the complete learning pipeline
- **Vision & Language**: Parallel tracks that converge at optimization
---
## Three-Tier Structure
```{mermaid}
%%{init: {'theme':'base', 'themeVariables': { 'fontSize':'16px'}}}%%
timeline
title TinyTorch Three-Tier Learning Journey
section 🏗️ Foundation
Module 01 : Tensor
Module 02 : Activations
Module 03 : Layers
Module 04 : Losses
Module 05 : Autograd
Module 06 : Optimizers
Module 07 : Training
section 🏛️ Architecture
Module 08 : DataLoader
Module 09 : Spatial (CNNs)
Module 10 : Tokenization
Module 11 : Embeddings
Module 12 : Attention
Module 13 : Transformers
section ⚡ Optimization
Module 14 : Profiling
Module 15 : Quantization
Module 16 : Compression
Module 17 : Memoization
Module 18 : Acceleration
Module 19 : Benchmarking
Module 20 : Competition
```
---
## Historical Milestones Timeline
```{mermaid}
%%{init: {'theme':'base', 'themeVariables': { 'primaryColor':'#fff9c4','primaryTextColor':'#f57c00','primaryBorderColor':'#fbc02d'}}}%%
gantt
title ML History Recreation Journey
dateFormat YYYY
axisFormat %Y
section Milestones
M01 1957 Perceptron :milestone, 1957, 0d
M02 1969 XOR Solution :milestone, 1969, 0d
M03 1986 MLP Revival :milestone, 1986, 0d
M04 1998 CNN Revolution :milestone, 1998, 0d
M05 2017 Transformer Era :milestone, 2017, 0d
M06 2024 Systems Age :milestone, 2024, 0d
section Your Progress
Foundation (M01-07) :active, 1957, 1969
Architecture (M08-13) :1969, 2017
Optimization (M14-20) :2017, 2024
```
**Journey Through ML History**: As you complete modules, you unlock milestones that recreate 67 years of machine learning breakthroughs using YOUR implementations.
---
## Student Learning Paths
```{mermaid}
%%{init: {'theme':'base'}}%%
flowchart TD
Start([Choose Your Path]) --> Decision{Learning Goal?}
Decision -->|"Fast: Understand ML"| Fast["🚀 Fast Track<br/>(6-8 weeks)<br/>Modules 01-09"]
Decision -->|"Deep: Build Everything"| Complete["🎯 Complete Builder<br/>(14-18 weeks)<br/>All 20 Modules"]
Decision -->|"Focus: Specific Skills"| Focused["🔍 Focused Explorer<br/>(8-12 weeks)<br/>Choose Tiers"]
Fast --> F1[Foundation<br/>01-07]
F1 --> F2[DataLoader<br/>08]
F2 --> F3[Spatial/CNNs<br/>09]
F3 --> FResult["✅ Can build & train<br/>neural networks<br/>75%+ CIFAR-10"]
Complete --> C1[Foundation<br/>01-07]
C1 --> C2[Architecture<br/>08-13]
C2 --> C3[Optimization<br/>14-20]
C3 --> CResult["🏆 ML Systems<br/>Engineer<br/>Production-ready"]
Focused --> Choice{Focus Area?}
Choice -->|Vision| FV[Foundation +<br/>Spatial 09]
Choice -->|Language| FL[Foundation +<br/>NLP 10-13]
Choice -->|Production| FO[Foundation +<br/>Optimization 14-20]
FV --> FVResult["✅ Computer<br/>Vision Expert"]
FL --> FLResult["✅ NLP/LLM<br/>Specialist"]
FO --> FOResult["✅ ML Optimization<br/>Engineer"]
style Fast fill:#e3f2fd,stroke:#2196f3
style Complete fill:#f3e5f5,stroke:#9c27b0
style Focused fill:#fff3e0,stroke:#f57c00
style FResult fill:#c8e6c9,stroke:#4caf50
style CResult fill:#fff9c4,stroke:#fbc02d,stroke-width:3px
style FVResult fill:#c8e6c9,stroke:#4caf50
style FLResult fill:#c8e6c9,stroke:#4caf50
style FOResult fill:#c8e6c9,stroke:#4caf50
```
---
## Capability Progression
```{mermaid}
%%{init: {'theme':'base', 'themeVariables': { 'primaryColor':'#e1f5fe','primaryTextColor':'#01579b'}}}%%
graph TB
subgraph L1["Level 1: Foundation"]
C1["Can create tensors<br/>and perform operations"]
C2["Can build neural<br/>network layers"]
C3["Can implement<br/>backpropagation"]
C1 --> C2 --> C3
end
subgraph L2["Level 2: Training"]
C4["Can train networks<br/>on datasets"]
C5["Can achieve 95%+<br/>on MNIST"]
C3 --> C4 --> C5
end
subgraph L3["Level 3: Architectures"]
C6["Can build CNNs<br/>for vision"]
C7["Can build transformers<br/>for language"]
C8["Can achieve 75%+<br/>on CIFAR-10"]
C5 --> C6 --> C8
C5 --> C7
end
subgraph L4["Level 4: Production"]
C9["Can profile and<br/>optimize models"]
C10["Can compress 4×<br/>and speedup 10×"]
C11["Can deploy production<br/>ML systems"]
C8 --> C9
C7 --> C9
C9 --> C10 --> C11
end
C11 --> Master["🎓 ML Systems<br/>Mastery"]
style C1 fill:#e3f2fd
style C3 fill:#fff3e0
style C5 fill:#f3e5f5
style C8 fill:#fff9c4,stroke:#fbc02d,stroke-width:3px
style C11 fill:#ffccbc
style Master fill:#c8e6c9,stroke:#4caf50,stroke-width:4px
```
**Each level builds concrete, measurable capabilities** - not just "completed a module" but "can build production CNNs achieving 75%+ accuracy."
---
## Workflow Cycle
```{mermaid}
%%{init: {'theme':'base', 'themeVariables': { 'primaryColor':'#f0f4c3'}}}%%
graph LR
Edit["📝 Edit Modules<br/>modules/source/XX_name/"] --> Export["⚙️ Export to Package<br/>tito module complete XX"]
Export --> Validate["✅ Validate with Milestones<br/>milestones/0X_*/script.py"]
Validate --> Check{Tests Pass?}
Check -->|Yes| Next["➡️ Next Module"]
Check -->|No| Debug["🔍 Debug & Fix"]
Debug --> Edit
Next --> Edit
Validate -.optional.-> Progress["📊 Track Progress<br/>tito checkpoint status"]
style Edit fill:#e3f2fd,stroke:#2196f3,stroke-width:2px
style Export fill:#fff3e0,stroke:#f57c00,stroke-width:2px
style Validate fill:#f3e5f5,stroke:#9c27b0,stroke-width:2px
style Next fill:#c8e6c9,stroke:#4caf50,stroke-width:2px
style Debug fill:#ffcdd2,stroke:#f44336,stroke-width:2px
style Progress fill:#f5f5f5,stroke:#9e9e9e,stroke-width:1px,stroke-dasharray: 5 5
```
**The essential three-step cycle**: Edit → Export → Validate
**📖 See [Student Workflow](student-workflow.html)** for detailed workflow guide.
---
## Dataset Strategy
```{mermaid}
%%{init: {'theme':'base', 'themeVariables': { 'primaryColor':'#e8eaf6'}}}%%
flowchart TB
Start([Start Learning]) --> Ship["📦 Shipped Datasets<br/>(~350 KB in repo)"]
Ship --> TD["TinyDigits<br/>1,200 samples<br/>8×8 images<br/>310 KB"]
Ship --> TT["TinyTalks<br/>350 Q&A pairs<br/>Character-level<br/>40 KB"]
TD --> M03["Milestone 03<br/>MLP on TinyDigits<br/>⚡ Fast iteration"]
TT --> M05["Milestone 05<br/>Transformers on TinyTalks<br/>⚡ Instant training"]
M03 --> Scale{Scale Up?}
M05 --> Scale
Scale -->|Yes| Download["⬇️ Downloaded Datasets<br/>(Auto-download when needed)"]
Download --> MNIST["MNIST<br/>70K samples<br/>28×28 images<br/>10 MB"]
Download --> CIFAR["CIFAR-10<br/>60K samples<br/>32×32 RGB<br/>170 MB"]
MNIST --> M03B["Milestone 03<br/>MLP on MNIST<br/>🎯 95%+ accuracy"]
CIFAR --> M04["Milestone 04<br/>CNN on CIFAR-10<br/>🏆 75%+ accuracy"]
style Ship fill:#c8e6c9,stroke:#4caf50,stroke-width:2px
style TD fill:#e3f2fd,stroke:#2196f3
style TT fill:#e3f2fd,stroke:#2196f3
style Download fill:#fff3e0,stroke:#f57c00,stroke-width:2px
style MNIST fill:#f3e5f5,stroke:#9c27b0
style CIFAR fill:#fff9c4,stroke:#fbc02d,stroke-width:3px
style M04 fill:#ffccbc,stroke:#ff5722,stroke-width:3px
```
**Strategy**: Start small (shipped datasets), iterate fast, then validate on benchmarks (downloaded datasets).
**📖 See [Datasets Guide](datasets.html)** for complete dataset documentation.
---
## Success Metrics
```{mermaid}
%%{init: {'theme':'base'}}%%
mindmap
root((TinyTorch<br/>Success))
Technical Skills
Build tensors from scratch
Implement autograd engine
Train real neural networks
Achieve 75%+ CIFAR-10
Optimize for production
Understanding
Know how PyTorch works internally
Understand gradient flow
Debug ML issues from first principles
Profile and optimize bottlenecks
Career Impact
ML Systems Engineer role-ready
Can implement novel architectures
Production deployment skills
Portfolio project (capstone)
Milestones Achieved
6 historical ML breakthroughs
Recreated 67 years of ML history
95%+ MNIST accuracy
75%+ CIFAR-10 accuracy
```
---
## Time Investment vs. Outcomes
```{mermaid}
%%{init: {'theme':'base'}}%%
quadrantChart
title Learning Paths: Time vs. Depth
x-axis "Time Investment (weeks)"
y-axis "ML Systems Mastery"
quadrant-1 "Complete Mastery"
quadrant-2 "Deep Understanding"
quadrant-3 "Quick Learning"
quadrant-4 "Focused Skills"
"Fast Track (6-8w)": [0.35, 0.5]
"Focused Vision (8w)": [0.45, 0.6]
"Focused NLP (10w)": [0.55, 0.65]
"Complete Builder (14-18w)": [0.85, 0.95]
"Foundation Only (4w)": [0.25, 0.35]
```
**Quadrants:**
- **Bottom-left (Quick Learning)**: Foundation tier - understand basics in 4 weeks
- **Top-left (Deep Understanding)**: Fast track - build & train networks in 6-8 weeks
- **Bottom-right (Focused Skills)**: Specialized paths - vision or NLP focus
- **Top-right (Complete Mastery)**: Full course - ML systems engineer in 14-18 weeks
---
## Module Difficulty Progression
```{mermaid}
%%{init: {'theme':'base'}}%%
%%{init: {'theme':'base', 'themeVariables': { 'xyChart': {'backgroundColor': 'transparent'}}}}%%
xychart-beta
title "Difficulty Curve Across 20 Modules"
x-axis [M01, M02, M03, M04, M05, M06, M07, M08, M09, M10, M11, M12, M13, M14, M15, M16, M17, M18, M19, M20]
y-axis "Difficulty (1-5 stars)" 0 --> 5
line [2, 2, 3, 3, 4, 4, 4, 3, 5, 4, 4, 5, 5, 4, 5, 5, 4, 4, 4, 5]
```
**Key observations:**
- **Gentle start**: Modules 01-02 are beginner-friendly
- **First challenge**: Module 05 (Autograd) - the critical breakthrough
- **Sustained difficulty**: Modules 09, 12, 13, 15-16 are advanced (⭐⭐⭐⭐⭐)
- **Capstone peak**: Module 20 integrates everything
---
## Ready to Start?
<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;">Begin Your Visual Journey</h3>
<p style="margin: 0 0 1.5rem 0; color: #6c757d;">These diagrams show the path - now walk it!</p>
<a href="quickstart-guide.html" style="display: inline-block; background: #007bff; color: white; padding: 0.75rem 1.5rem; border-radius: 0.25rem; text-decoration: none; font-weight: 500; margin-right: 1rem;">Start Building →</a>
<a href="learning-progress.html" style="display: inline-block; background: #28a745; color: white; padding: 0.75rem 1.5rem; border-radius: 0.25rem; text-decoration: none; font-weight: 500;">Track Progress →</a>
</div>
---
## 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