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TinyTorch: Build ML Systems from Scratch
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<h1>Visual Learning Journey</h1>
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<h2> Contents </h2>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-complete-learning-flow">The Complete Learning Flow</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-dependencies">Module Dependencies</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#three-tier-structure">Three-Tier Structure</a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#historical-milestones-timeline">Historical Milestones Timeline</a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#student-learning-paths">Student Learning Paths</a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#capability-progression">Capability Progression</a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#workflow-cycle">Workflow Cycle</a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#dataset-strategy">Dataset Strategy</a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#success-metrics">Success Metrics</a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#time-investment-vs-outcomes">Time Investment vs. Outcomes</a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-difficulty-progression">Module Difficulty Progression</a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#ready-to-start">Ready to Start?</a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#related-pages">Related Pages</a></li>
|
||
</ul>
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div id="searchbox"></div>
|
||
<article class="bd-article">
|
||
|
||
<section id="visual-learning-journey">
|
||
<h1>Visual Learning Journey<a class="headerlink" href="#visual-learning-journey" title="Permalink to this heading">#</a></h1>
|
||
<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>
|
||
<p><strong>Purpose</strong>: Visualize the learning progression, module dependencies, and milestone achievements in TinyTorch.</p>
|
||
<hr class="docutils" />
|
||
<section id="the-complete-learning-flow">
|
||
<h2>The Complete Learning Flow<a class="headerlink" href="#the-complete-learning-flow" title="Permalink to this heading">#</a></h2>
|
||
<pre class="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
|
||
</pre><p><strong>Legend:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>🟦 Blue: Foundation modules</p></li>
|
||
<li><p>🟧 Orange highlights: Critical modules (Autograd, Training)</p></li>
|
||
<li><p>🟪 Purple: Advanced architecture modules</p></li>
|
||
<li><p>🟩 Green: Milestone achievements</p></li>
|
||
<li><p>🟨 Yellow: North Star milestone (CIFAR-10)</p></li>
|
||
<li><p>🟥 Red: Capstone</p></li>
|
||
</ul>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="module-dependencies">
|
||
<h2>Module Dependencies<a class="headerlink" href="#module-dependencies" title="Permalink to this heading">#</a></h2>
|
||
<pre class="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
|
||
</pre><p><strong>Key Dependencies:</strong></p>
|
||
<ul class="simple">
|
||
<li><p><strong>Tensor (Module 01)</strong>: Foundation for everything - all modules depend on it</p></li>
|
||
<li><p><strong>Autograd (Module 05)</strong>: Enhances Tensor, enables all learning</p></li>
|
||
<li><p><strong>Training (Module 07)</strong>: Orchestrates the complete learning pipeline</p></li>
|
||
<li><p><strong>Vision & Language</strong>: Parallel tracks that converge at optimization</p></li>
|
||
</ul>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="three-tier-structure">
|
||
<h2>Three-Tier Structure<a class="headerlink" href="#three-tier-structure" title="Permalink to this heading">#</a></h2>
|
||
<pre class="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
|
||
</pre></section>
|
||
<hr class="docutils" />
|
||
<section id="historical-milestones-timeline">
|
||
<h2>Historical Milestones Timeline<a class="headerlink" href="#historical-milestones-timeline" title="Permalink to this heading">#</a></h2>
|
||
<pre class="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
|
||
</pre><p><strong>Journey Through ML History</strong>: As you complete modules, you unlock milestones that recreate 67 years of machine learning breakthroughs using YOUR implementations.</p>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="student-learning-paths">
|
||
<h2>Student Learning Paths<a class="headerlink" href="#student-learning-paths" title="Permalink to this heading">#</a></h2>
|
||
<pre class="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
|
||
</pre></section>
|
||
<hr class="docutils" />
|
||
<section id="capability-progression">
|
||
<h2>Capability Progression<a class="headerlink" href="#capability-progression" title="Permalink to this heading">#</a></h2>
|
||
<pre class="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
|
||
</pre><p><strong>Each level builds concrete, measurable capabilities</strong> - not just “completed a module” but “can build production CNNs achieving 75%+ accuracy.”</p>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="workflow-cycle">
|
||
<h2>Workflow Cycle<a class="headerlink" href="#workflow-cycle" title="Permalink to this heading">#</a></h2>
|
||
<pre class="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
|
||
</pre><p><strong>The essential three-step cycle</strong>: Edit → Export → Validate</p>
|
||
<p><strong>📖 See <span class="xref myst">Student Workflow</span></strong> for detailed workflow guide.</p>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="dataset-strategy">
|
||
<h2>Dataset Strategy<a class="headerlink" href="#dataset-strategy" title="Permalink to this heading">#</a></h2>
|
||
<pre class="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
|
||
</pre><p><strong>Strategy</strong>: Start small (shipped datasets), iterate fast, then validate on benchmarks (downloaded datasets).</p>
|
||
<p><strong>📖 See <span class="xref myst">Datasets Guide</span></strong> for complete dataset documentation.</p>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="success-metrics">
|
||
<h2>Success Metrics<a class="headerlink" href="#success-metrics" title="Permalink to this heading">#</a></h2>
|
||
<pre class="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
|
||
</pre></section>
|
||
<hr class="docutils" />
|
||
<section id="time-investment-vs-outcomes">
|
||
<h2>Time Investment vs. Outcomes<a class="headerlink" href="#time-investment-vs-outcomes" title="Permalink to this heading">#</a></h2>
|
||
<pre class="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]
|
||
</pre><p><strong>Quadrants:</strong></p>
|
||
<ul class="simple">
|
||
<li><p><strong>Bottom-left (Quick Learning)</strong>: Foundation tier - understand basics in 4 weeks</p></li>
|
||
<li><p><strong>Top-left (Deep Understanding)</strong>: Fast track - build & train networks in 6-8 weeks</p></li>
|
||
<li><p><strong>Bottom-right (Focused Skills)</strong>: Specialized paths - vision or NLP focus</p></li>
|
||
<li><p><strong>Top-right (Complete Mastery)</strong>: Full course - ML systems engineer in 14-18 weeks</p></li>
|
||
</ul>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="module-difficulty-progression">
|
||
<h2>Module Difficulty Progression<a class="headerlink" href="#module-difficulty-progression" title="Permalink to this heading">#</a></h2>
|
||
<pre class="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]
|
||
</pre><p><strong>Key observations:</strong></p>
|
||
<ul class="simple">
|
||
<li><p><strong>Gentle start</strong>: Modules 01-02 are beginner-friendly</p></li>
|
||
<li><p><strong>First challenge</strong>: Module 05 (Autograd) - the critical breakthrough</p></li>
|
||
<li><p><strong>Sustained difficulty</strong>: Modules 09, 12, 13, 15-16 are advanced (⭐⭐⭐⭐⭐)</p></li>
|
||
<li><p><strong>Capstone peak</strong>: Module 20 integrates everything</p></li>
|
||
</ul>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="ready-to-start">
|
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<h2>Ready to Start?<a class="headerlink" href="#ready-to-start" title="Permalink to this heading">#</a></h2>
|
||
<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>
|
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<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>
|
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|
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|
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<hr class="docutils" />
|
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<section id="related-pages">
|
||
<h2>Related Pages<a class="headerlink" href="#related-pages" title="Permalink to this heading">#</a></h2>
|
||
<ul class="simple">
|
||
<li><p><strong>📖 <span class="xref myst">Introduction</span></strong> - What is TinyTorch and why build from scratch</p></li>
|
||
<li><p><strong>📖 <span class="xref myst">Student Workflow</span></strong> - The essential edit → export → validate cycle</p></li>
|
||
<li><p><strong>📖 <span class="xref myst">Three-Tier Structure</span></strong> - Detailed tier breakdown</p></li>
|
||
<li><p><strong>📖 <span class="xref myst">Milestones</span></strong> - Journey through ML history</p></li>
|
||
<li><p><strong>📖 <span class="xref myst">FAQ</span></strong> - Common questions answered</p></li>
|
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