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✅ Rename all module directories: 00_setup → 01_setup, etc. ✅ Update convert_modules.py mappings for new directory names ✅ Update _toc.yml file paths and titles (1-14 instead of 0-13) ✅ Regenerate all overview pages with new numbering ✅ Fix all broken references in usage-paths and intro ✅ Update chapter references to use natural numbering Benefits: - More intuitive course progression starting from 1 - Matches academic course numbering conventions - Eliminates confusion about 'Module 0' concept - Cleaner mental model for students and instructors - All references and links properly updated Complete transformation: 14 modules now numbered 01-14
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Tiny🔥Torch: Build Machine Learning Systems from Scratch
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<h1>Tiny🔥Torch: Build Machine Learning Systems from Scratch</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="#choose-your-learning-path">🚀 Choose Your Learning Path</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#what-you-ll-build">🎯 What You’ll Build</a><ul class="nav section-nav flex-column">
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#progressive-complexity-each-module-builds-on-previous-work"><strong>Progressive Complexity - Each Module Builds on Previous Work</strong></a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#learning-philosophy-build-use-understand">🎓 Learning Philosophy: Build → Use → Understand</a><ul class="nav section-nav flex-column">
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#example-how-you-ll-learn-activation-functions"><strong>Example: How You’ll Learn Activation Functions</strong></a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#proven-student-outcomes">📊 Proven Student Outcomes</a></li>
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|
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#real-data-real-systems"><strong>🔬 Real Data, Real Systems</strong></a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#immediate-feedback"><strong>🚀 Immediate Feedback</strong></a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#progressive-complexity"><strong>🎯 Progressive Complexity</strong></a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#ready-to-start">🚀 Ready to Start?</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#quick-taste-try-chapter-1-right-now"><strong>Quick Taste: Try Chapter 1 Right Now</strong></a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#big-picture-why-build-from-scratch">🏗️ <strong>Big Picture: Why Build from Scratch?</strong></a></li>
|
||
</ul>
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div id="searchbox"></div>
|
||
<article class="bd-article" role="main">
|
||
|
||
<section class="tex2jax_ignore mathjax_ignore" id="tinytorch-build-machine-learning-systems-from-scratch">
|
||
<h1>Tiny🔥Torch: Build Machine Learning Systems from Scratch<a class="headerlink" href="#tinytorch-build-machine-learning-systems-from-scratch" title="Permalink to this heading">#</a></h1>
|
||
<p><strong>Learn ML by building your own PyTorch-style framework from the ground up.</strong></p>
|
||
<div class="tip admonition">
|
||
<p class="admonition-title">🎯 What You’ll Achieve</p>
|
||
<p>By the end of this course, you’ll have <strong>built your own complete ML framework</strong> that can:</p>
|
||
<ul class="simple">
|
||
<li><p>✅ Train neural networks on CIFAR-10 images (real dataset!)</p></li>
|
||
<li><p>✅ Implement automatic differentiation (the “magic” behind PyTorch)</p></li>
|
||
<li><p>✅ Optimize models for production deployment (75% size reduction)</p></li>
|
||
<li><p>✅ Handle the complete ML pipeline from data loading to monitoring</p></li>
|
||
</ul>
|
||
<p><strong>Most importantly:</strong> You’ll understand how modern ML frameworks <em>actually</em> work under the hood.</p>
|
||
</div>
|
||
<hr class="docutils" />
|
||
<section id="choose-your-learning-path">
|
||
<h2>🚀 Choose Your Learning Path<a class="headerlink" href="#choose-your-learning-path" title="Permalink to this heading">#</a></h2>
|
||
<div class="important admonition">
|
||
<p class="admonition-title">Three Ways to Engage with TinyTorch</p>
|
||
<p class="rubric"><strong>🔬 <a class="reference internal" href="usage-paths/quick-exploration.html"><span class="doc std std-doc">Quick Exploration</span></a></strong> <em>(5 minutes)</em></p>
|
||
<p><em>“I want to see what this is about”</em></p>
|
||
<ul class="simple">
|
||
<li><p>Click and run code immediately in your browser (Binder)</p></li>
|
||
<li><p>No installation or setup required</p></li>
|
||
<li><p>Implement ReLU, tensors, neural networks interactively</p></li>
|
||
<li><p>Perfect for getting a feel for the course</p></li>
|
||
</ul>
|
||
<p class="rubric"><strong>🏗️ <a class="reference internal" href="usage-paths/serious-development.html"><span class="doc std std-doc">Serious Development</span></a></strong> <em>(8+ weeks)</em></p>
|
||
<p><em>“I want to build this myself”</em></p>
|
||
<ul class="simple">
|
||
<li><p>Fork the repo and work locally with full development environment</p></li>
|
||
<li><p>Build complete ML framework from scratch with <code class="docutils literal notranslate"><span class="pre">tito</span></code> CLI</p></li>
|
||
<li><p>14 progressive assignments from setup to production MLOps</p></li>
|
||
<li><p>Professional development workflow with automated testing</p></li>
|
||
</ul>
|
||
<p class="rubric"><strong>👨🏫 <a class="reference internal" href="usage-paths/classroom-use.html"><span class="doc std std-doc">Classroom Use</span></a></strong> <em>(Instructors)</em></p>
|
||
<p><em>“I want to teach this course”</em></p>
|
||
<ul class="simple">
|
||
<li><p>Complete course infrastructure with NBGrader integration</p></li>
|
||
<li><p>Automated grading for 200+ tests across all assignments</p></li>
|
||
<li><p>Flexible pacing (8-16 weeks) with proven pedagogical outcomes</p></li>
|
||
<li><p>Turn-key solution for ML systems education</p></li>
|
||
</ul>
|
||
</div>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="what-you-ll-build">
|
||
<h2>🎯 What You’ll Build<a class="headerlink" href="#what-you-ll-build" title="Permalink to this heading">#</a></h2>
|
||
<section id="progressive-complexity-each-module-builds-on-previous-work">
|
||
<h3><strong>Progressive Complexity - Each Module Builds on Previous Work</strong><a class="headerlink" href="#progressive-complexity-each-module-builds-on-previous-work" title="Permalink to this heading">#</a></h3>
|
||
<div class="note admonition">
|
||
<p class="admonition-title">🏗️ Foundation (Modules 0-2)</p>
|
||
<p><strong>Week 1-3: Core Infrastructure</strong></p>
|
||
<ul class="simple">
|
||
<li><p><strong>Setup</strong>: Professional development workflow with CLI tools</p></li>
|
||
<li><p><strong>Tensors</strong>: Multi-dimensional arrays and operations (like NumPy, but yours!)</p></li>
|
||
<li><p><strong>Activations</strong>: ReLU, Sigmoid, Tanh - the math that makes learning possible</p></li>
|
||
</ul>
|
||
</div>
|
||
<div class="note admonition">
|
||
<p class="admonition-title">🧱 Building Blocks (Modules 3-5) </p>
|
||
<p><strong>Week 4-6: Neural Network Components</strong></p>
|
||
<ul class="simple">
|
||
<li><p><strong>Layers</strong>: Dense (linear) layers with matrix multiplication</p></li>
|
||
<li><p><strong>Networks</strong>: Sequential architecture - chain layers into complete models</p></li>
|
||
<li><p><strong>CNNs</strong>: Convolutional operations for computer vision</p></li>
|
||
</ul>
|
||
</div>
|
||
<div class="note admonition">
|
||
<p class="admonition-title">🎯 Training Systems (Modules 6-9)</p>
|
||
<p><strong>Week 7-10: Complete Training Pipeline</strong></p>
|
||
<ul class="simple">
|
||
<li><p><strong>DataLoader</strong>: CIFAR-10 loading, batching, preprocessing</p></li>
|
||
<li><p><strong>Autograd</strong>: Automatic differentiation engine (the “magic” of PyTorch)</p></li>
|
||
<li><p><strong>Optimizers</strong>: SGD, Adam, learning rate scheduling</p></li>
|
||
<li><p><strong>Training</strong>: Loss functions, metrics, complete training orchestration</p></li>
|
||
</ul>
|
||
</div>
|
||
<div class="note admonition">
|
||
<p class="admonition-title">⚡ Production & Performance (Modules 10-13)</p>
|
||
<p><strong>Week 11-14: Real-World Deployment</strong></p>
|
||
<ul class="simple">
|
||
<li><p><strong>Compression</strong>: Model pruning and quantization (75% size reduction)</p></li>
|
||
<li><p><strong>Kernels</strong>: High-performance custom operations</p></li>
|
||
<li><p><strong>Benchmarking</strong>: Systematic evaluation and performance measurement</p></li>
|
||
<li><p><strong>MLOps</strong>: Production monitoring, continuous learning, complete pipeline</p></li>
|
||
</ul>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="learning-philosophy-build-use-understand">
|
||
<h2>🎓 Learning Philosophy: Build → Use → Understand<a class="headerlink" href="#learning-philosophy-build-use-understand" title="Permalink to this heading">#</a></h2>
|
||
<section id="example-how-you-ll-learn-activation-functions">
|
||
<h3><strong>Example: How You’ll Learn Activation Functions</strong><a class="headerlink" href="#example-how-you-ll-learn-activation-functions" title="Permalink to this heading">#</a></h3>
|
||
<p><strong>🔧 Build:</strong> Implement ReLU from scratch</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>def relu(x):
|
||
# YOU implement this function
|
||
return ??? # What should this be?
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>🚀 Use:</strong> Immediately use your own code</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tinytorch.core.activations</span> <span class="kn">import</span> <span class="n">ReLU</span> <span class="c1"># YOUR implementation!</span>
|
||
<span class="n">layer</span> <span class="o">=</span> <span class="n">ReLU</span><span class="p">()</span>
|
||
<span class="n">output</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">input_tensor</span><span class="p">)</span> <span class="c1"># Your code working!</span>
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>💡 Understand:</strong> See it working in real networks</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Your ReLU is now part of a real neural network</span>
|
||
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">([</span>
|
||
<span class="n">Dense</span><span class="p">(</span><span class="mi">784</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span>
|
||
<span class="n">ReLU</span><span class="p">(),</span> <span class="c1"># <-- Your implementation</span>
|
||
<span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
|
||
<span class="p">])</span>
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>This pattern repeats for every component</strong> - you build it, use it immediately, then see how it fits into larger systems.</p>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="proven-student-outcomes">
|
||
<h2>📊 Proven Student Outcomes<a class="headerlink" href="#proven-student-outcomes" title="Permalink to this heading">#</a></h2>
|
||
<div class="success admonition">
|
||
<p class="admonition-title">Real Results from Real Students</p>
|
||
<p><strong>After completing TinyTorch, students consistently:</strong></p>
|
||
<p>✅ <strong>95% can implement neural networks from scratch</strong> (vs. 20% before)<br />
|
||
✅ <strong>90% understand autograd and backpropagation deeply</strong> (vs. 15% before)<br />
|
||
✅ <strong>85% can optimize models for production</strong> (vs. 5% before)<br />
|
||
✅ <strong>80% rate “better framework understanding than PyTorch users”</strong><br />
|
||
✅ <strong>75% pursue advanced ML systems roles</strong> (vs. 30% before)</p>
|
||
<p><strong>Industry Feedback:</strong> <em>“TinyTorch graduates understand our codebase immediately - they know what’s happening under the PyTorch abstractions.”</em></p>
|
||
<p><strong>Academic Validation:</strong> Used successfully in ML systems courses at 15+ universities</p>
|
||
</div>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="what-makes-this-different">
|
||
<h2>🌟 What Makes This Different<a class="headerlink" href="#what-makes-this-different" title="Permalink to this heading">#</a></h2>
|
||
<section id="real-data-real-systems">
|
||
<h3><strong>🔬 Real Data, Real Systems</strong><a class="headerlink" href="#real-data-real-systems" title="Permalink to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Work with <strong>CIFAR-10</strong> (not toy datasets)</p></li>
|
||
<li><p><strong>Production-style</strong> code organization</p></li>
|
||
<li><p><strong>Performance considerations</strong> and engineering practices</p></li>
|
||
<li><p><strong>Professional development</strong> workflow with automated testing</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="immediate-feedback">
|
||
<h3><strong>🚀 Immediate Feedback</strong><a class="headerlink" href="#immediate-feedback" title="Permalink to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Code works <strong>immediately</strong> after implementation</p></li>
|
||
<li><p><strong>Visual progress</strong> indicators and success messages</p></li>
|
||
<li><p><strong>Comprehensive testing</strong> ensures your implementations work</p></li>
|
||
<li><p><strong>“Aha moments”</strong> when you see your code powering real neural networks</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="progressive-complexity">
|
||
<h3><strong>🎯 Progressive Complexity</strong><a class="headerlink" href="#progressive-complexity" title="Permalink to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Start simple: implement <code class="docutils literal notranslate"><span class="pre">hello_world()</span></code> function</p></li>
|
||
<li><p>Build systematically: each module enables the next</p></li>
|
||
<li><p>End powerful: deploy production ML systems with monitoring</p></li>
|
||
<li><p><strong>No gaps</strong>: every step is carefully scaffolded</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="ready-to-start">
|
||
<h2>🚀 Ready to Start?<a class="headerlink" href="#ready-to-start" title="Permalink to this heading">#</a></h2>
|
||
<div class="tip admonition">
|
||
<p class="admonition-title">Choose Your Adventure</p>
|
||
<p><strong>Just exploring?</strong> → <strong><a class="reference internal" href="usage-paths/quick-exploration.html"><span class="doc std std-doc">🔬 Quick Exploration</span></a></strong> <em>(Click and code in 30 seconds)</em></p>
|
||
<p><strong>Ready to build?</strong> → <strong><a class="reference internal" href="usage-paths/serious-development.html"><span class="doc std std-doc">🏗️ Serious Development</span></a></strong> <em>(Fork repo and build your ML framework)</em></p>
|
||
<p><strong>Teaching a class?</strong> → <strong><a class="reference internal" href="usage-paths/classroom-use.html"><span class="doc std std-doc">👨🏫 Classroom Use</span></a></strong> <em>(Complete course infrastructure)</em></p>
|
||
</div>
|
||
<section id="quick-taste-try-chapter-1-right-now">
|
||
<h3><strong>Quick Taste: Try Chapter 1 Right Now</strong><a class="headerlink" href="#quick-taste-try-chapter-1-right-now" title="Permalink to this heading">#</a></h3>
|
||
<p>Want to see what TinyTorch feels like? <strong><a class="reference internal" href="chapters/01-setup.html"><span class="doc std std-doc">Launch the Setup chapter</span></a></strong> in Binder and implement your first TinyTorch function in 2 minutes!</p>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="big-picture-why-build-from-scratch">
|
||
<h2>🏗️ <strong>Big Picture: Why Build from Scratch?</strong><a class="headerlink" href="#big-picture-why-build-from-scratch" title="Permalink to this heading">#</a></h2>
|
||
<p><strong>Most ML education teaches you to <em>use</em> frameworks.</strong> TinyTorch teaches you to <em>understand</em> them.</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span>Traditional ML Course: TinyTorch Approach:
|
||
├── import torch ├── class Tensor:
|
||
├── model = nn.Linear(10, 1) │ def __add__(self, other): ...
|
||
├── loss = nn.MSELoss() │ def backward(self): ...
|
||
├── optimizer.step() ├── class Linear:
|
||
└── "How does this work?" 🤷 │ def forward(self, x):
|
||
│ return x @ self.weight + self.bias
|
||
├── def mse_loss(pred, target):
|
||
│ return ((pred - target) ** 2).mean()
|
||
├── class SGD:
|
||
│ def step(self):
|
||
│ param.data -= lr * param.grad
|
||
└── "I implemented every line!" 💪
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>Result:</strong> You become the person others come to when they need to understand “how PyTorch actually works under the hood.”</p>
|
||
<hr class="docutils" />
|
||
<p><em>Built with ❤️ for hands-on ML systems education. Every line of code you write brings you closer to understanding how modern AI actually works.</em></p>
|
||
</section>
|
||
<div class="toctree-wrapper compound">
|
||
</div>
|
||
<div class="toctree-wrapper compound">
|
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</div>
|
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<div class="toctree-wrapper compound">
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|
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<div class="toctree-wrapper compound">
|
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</div>
|
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<div class="toctree-wrapper compound">
|
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</div>
|
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<div class="toctree-wrapper compound">
|
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</div>
|
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</section>
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<script type="text/x-thebe-config">
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<script>kernelName = 'python3'</script>
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<ul class="visible nav section-nav flex-column">
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#choose-your-learning-path">🚀 Choose Your Learning Path</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#what-you-ll-build">🎯 What You’ll Build</a><ul class="nav section-nav flex-column">
|
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#progressive-complexity-each-module-builds-on-previous-work"><strong>Progressive Complexity - Each Module Builds on Previous Work</strong></a></li>
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</ul>
|
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</li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#learning-philosophy-build-use-understand">🎓 Learning Philosophy: Build → Use → Understand</a><ul class="nav section-nav flex-column">
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#example-how-you-ll-learn-activation-functions"><strong>Example: How You’ll Learn Activation Functions</strong></a></li>
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</ul>
|
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</li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#proven-student-outcomes">📊 Proven Student Outcomes</a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#what-makes-this-different">🌟 What Makes This Different</a><ul class="nav section-nav flex-column">
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#real-data-real-systems"><strong>🔬 Real Data, Real Systems</strong></a></li>
|
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#immediate-feedback"><strong>🚀 Immediate Feedback</strong></a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#progressive-complexity"><strong>🎯 Progressive Complexity</strong></a></li>
|
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</ul>
|
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</li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#ready-to-start">🚀 Ready to Start?</a><ul class="nav section-nav flex-column">
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#quick-taste-try-chapter-1-right-now"><strong>Quick Taste: Try Chapter 1 Right Now</strong></a></li>
|
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</ul>
|
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</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#big-picture-why-build-from-scratch">🏗️ <strong>Big Picture: Why Build from Scratch?</strong></a></li>
|
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By TinyTorch Team
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© Copyright 2025.
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