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</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div id="jb-print-docs-body" class="onlyprint">
|
||
<h1>Course Introduction: ML Systems Engineering Through Implementation</h1>
|
||
<!-- Table of contents -->
|
||
<div id="print-main-content">
|
||
<div id="jb-print-toc">
|
||
|
||
<div>
|
||
<h2> Contents </h2>
|
||
</div>
|
||
<nav aria-label="Page">
|
||
<ul class="visible nav section-nav flex-column">
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-origin-story-why-tinytorch-exists">The Origin Story: Why TinyTorch Exists</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-problem-were-solving">The Problem We’re Solving</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#how-tinytorch-began">How TinyTorch Began</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#our-solution-learn-by-building">Our Solution: Learn By Building</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#core-learning-concepts">Core Learning Concepts</a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#what-makes-tinytorch-different">What Makes TinyTorch Different</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#the-learning-philosophy-build-use-reflect">The Learning Philosophy: Build → Use → Reflect</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#who-this-course-serves">Who This Course Serves</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#perfect-for">Perfect For:</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#prerequisites">Prerequisites</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#what-youll-achieve-tier-by-tier-mastery">What You’ll Achieve: Tier-by-Tier Mastery</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#after-foundation-tier-modules-01-07">After Foundation Tier (Modules 01-07)</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#after-architecture-tier-modules-08-13">After Architecture Tier (Modules 08-13)</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#after-optimization-tier-modules-14-20">After Optimization Tier (Modules 14-20)</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-ml-evolution-story-youll-experience">The ML Evolution Story You’ll Experience</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#foundation-era-1980s-1990s-foundation-tier">Foundation Era (1980s-1990s) → Foundation Tier</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#architecture-era-1990s-2010s-architecture-tier">Architecture Era (1990s-2010s) → Architecture Tier</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#optimization-era-2010s-present-optimization-tier">Optimization Era (2010s-Present) → Optimization Tier</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#systems-engineering-focus-why-tiers-matter">Systems Engineering Focus: Why Tiers Matter</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#traditional-linear-approach">Traditional Linear Approach:</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#tinytorch-tier-approach">TinyTorch Tier Approach:</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#what-traditional-courses-teach-vs-tinytorch-tiers">What Traditional Courses Teach vs. TinyTorch Tiers:</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#career-impact-by-tier">Career Impact by Tier</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#learning-support-community">Learning Support & Community</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#comprehensive-infrastructure">Comprehensive Infrastructure</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#multiple-learning-paths">Multiple Learning Paths</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#professional-development-practices">Professional Development Practices</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#start-your-journey">Start Your Journey</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#foundation-tier-modules-01-07">Foundation Tier (Modules 01-07)</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#architecture-tier-modules-08-13">Architecture Tier (Modules 08-13)</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#optimization-tier-modules-14-19">Optimization Tier (Modules 14-19)</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#learning-path-recommendations">Learning Path Recommendations</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#choose-your-learning-style">Choose Your Learning Style</a></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div id="searchbox"></div>
|
||
<article class="bd-article">
|
||
|
||
<section id="course-introduction-ml-systems-engineering-through-implementation">
|
||
<h1>Course Introduction: ML Systems Engineering Through Implementation<a class="headerlink" href="#course-introduction-ml-systems-engineering-through-implementation" title="Link to this heading">#</a></h1>
|
||
<p><strong>Transform from ML user to ML systems engineer by building everything yourself.</strong></p>
|
||
<hr class="docutils" />
|
||
<section id="the-origin-story-why-tinytorch-exists">
|
||
<h2>The Origin Story: Why TinyTorch Exists<a class="headerlink" href="#the-origin-story-why-tinytorch-exists" title="Link to this heading">#</a></h2>
|
||
<section id="the-problem-were-solving">
|
||
<h3>The Problem We’re Solving<a class="headerlink" href="#the-problem-were-solving" title="Link to this heading">#</a></h3>
|
||
<p>There’s a critical gap in ML engineering today. Plenty of people can use ML frameworks (PyTorch, TensorFlow, JAX, etc.), but very few understand the systems underneath. This creates real problems:</p>
|
||
<ul class="simple">
|
||
<li><p><strong>Engineers deploy models</strong> but can’t debug when things go wrong</p></li>
|
||
<li><p><strong>Teams hit performance walls</strong> because no one understands the bottlenecks</p></li>
|
||
<li><p><strong>Companies struggle to scale</strong> - whether to tiny edge devices or massive clusters</p></li>
|
||
<li><p><strong>Innovation stalls</strong> when everyone is limited to existing framework capabilities</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="how-tinytorch-began">
|
||
<h3>How TinyTorch Began<a class="headerlink" href="#how-tinytorch-began" title="Link to this heading">#</a></h3>
|
||
<p>TinyTorch started as exercises for the <a class="reference external" href="https://mlsysbook.ai">MLSysBook.ai</a> textbook - students needed hands-on implementation experience. But it quickly became clear this addressed a much bigger problem:</p>
|
||
<p><strong>The industry desperately needs engineers who can BUILD ML systems, not just USE them.</strong></p>
|
||
<p>Deploying ML systems at scale is hard. Scale means both directions:</p>
|
||
<ul class="simple">
|
||
<li><p><strong>Small scale</strong>: Running models on edge devices with 1MB of RAM</p></li>
|
||
<li><p><strong>Large scale</strong>: Training models across thousands of GPUs</p></li>
|
||
<li><p><strong>Production scale</strong>: Serving millions of requests with <100ms latency</p></li>
|
||
</ul>
|
||
<p>We need more engineers who understand memory hierarchies, computational graphs, kernel optimization, distributed communication - the actual systems that make ML work.</p>
|
||
</section>
|
||
<section id="our-solution-learn-by-building">
|
||
<h3>Our Solution: Learn By Building<a class="headerlink" href="#our-solution-learn-by-building" title="Link to this heading">#</a></h3>
|
||
<p>TinyTorch teaches ML systems the only way that really works: <strong>by building them yourself</strong>.</p>
|
||
<p>When you implement your own tensor operations, write your own autograd, build your own optimizer - you gain understanding that’s impossible to achieve by just calling APIs. You learn not just what these systems do, but HOW they do it and WHY they’re designed that way.</p>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="core-learning-concepts">
|
||
<h2>Core Learning Concepts<a class="headerlink" href="#core-learning-concepts" title="Link to this heading">#</a></h2>
|
||
<div style="background: #f7fafc; border: 1px solid #e2e8f0; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0;">
|
||
<p><strong>Concept 1: Systems Memory Analysis</strong></p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Learning objective: Understand memory usage patterns</span>
|
||
<span class="c1"># Framework user: "torch.optim.Adam()" - black box</span>
|
||
<span class="c1"># TinyTorch student: Implements Adam and discovers why it needs 3x parameter memory</span>
|
||
<span class="c1"># Result: Deep understanding of optimizer trade-offs applicable to any framework</span>
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>Concept 2: Computational Complexity</strong></p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Learning objective: Analyze algorithmic scaling behavior</span>
|
||
<span class="c1"># Framework user: "Attention mechanism" - abstract concept</span>
|
||
<span class="c1"># TinyTorch student: Implements attention from scratch, measures O(n²) scaling</span>
|
||
<span class="c1"># Result: Intuition for sequence modeling limits across PyTorch, TensorFlow, JAX</span>
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>Concept 3: Automatic Differentiation</strong></p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Learning objective: Understand gradient computation</span>
|
||
<span class="c1"># Framework user: "loss.backward()" - mysterious process</span>
|
||
<span class="c1"># TinyTorch student: Builds autograd engine with computational graphs</span>
|
||
<span class="c1"># Result: Knowledge of how all modern ML frameworks enable learning</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="what-makes-tinytorch-different">
|
||
<h2>What Makes TinyTorch Different<a class="headerlink" href="#what-makes-tinytorch-different" title="Link to this heading">#</a></h2>
|
||
<p>Most ML education teaches you to <strong>use</strong> frameworks (PyTorch, TensorFlow, JAX, etc.). TinyTorch teaches you to <strong>build</strong> them.</p>
|
||
<p>This fundamental difference creates engineers who understand systems deeply, not just APIs superficially.</p>
|
||
<section id="the-learning-philosophy-build-use-reflect">
|
||
<h3>The Learning Philosophy: Build → Use → Reflect<a class="headerlink" href="#the-learning-philosophy-build-use-reflect" title="Link to this heading">#</a></h3>
|
||
<p><strong>Traditional Approach:</strong></p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
|
||
<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">784</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="c1"># Use someone else's implementation</span>
|
||
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> <span class="c1"># Trust it works, don't understand how</span>
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>TinyTorch Approach:</strong></p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># 1. BUILD: You implement Linear from scratch</span>
|
||
<span class="k">class</span><span class="w"> </span><span class="nc">Linear</span><span class="p">:</span>
|
||
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||
<span class="k">return</span> <span class="n">x</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="c1"># You write this</span>
|
||
|
||
<span class="c1"># 2. USE: Your implementation in action</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">tinytorch.core.layers</span><span class="w"> </span><span class="kn">import</span> <span class="n">Linear</span> <span class="c1"># YOUR code</span>
|
||
<span class="n">model</span> <span class="o">=</span> <span class="n">Linear</span><span class="p">(</span><span class="mi">784</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="c1"># YOUR implementation</span>
|
||
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> <span class="c1"># YOU know exactly how this works</span>
|
||
|
||
<span class="c1"># 3. REFLECT: Systems thinking</span>
|
||
<span class="c1"># "Why does matrix multiplication dominate compute time?"</span>
|
||
<span class="c1"># "How does this scale with larger models?"</span>
|
||
<span class="c1"># "What memory optimizations are possible?"</span>
|
||
</pre></div>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="who-this-course-serves">
|
||
<h2>Who This Course Serves<a class="headerlink" href="#who-this-course-serves" title="Link to this heading">#</a></h2>
|
||
<section id="perfect-for">
|
||
<h3>Perfect For:<a class="headerlink" href="#perfect-for" title="Link to this heading">#</a></h3>
|
||
<p><strong>🎓 Computer Science Students</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Want to understand ML systems beyond high-level APIs</p></li>
|
||
<li><p>Need to implement custom operations for research</p></li>
|
||
<li><p>Preparing for ML engineering roles that require systems knowledge</p></li>
|
||
</ul>
|
||
<p><strong>👩💻 Software Engineers → ML Engineers</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Transitioning into ML engineering roles</p></li>
|
||
<li><p>Need to debug and optimize production ML systems</p></li>
|
||
<li><p>Want to understand what happens “under the hood” of ML frameworks</p></li>
|
||
</ul>
|
||
<p><strong>🔬 ML Practitioners & Researchers</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Debug performance issues in production systems</p></li>
|
||
<li><p>Implement novel architectures and custom operations</p></li>
|
||
<li><p>Optimize training and inference for resource constraints</p></li>
|
||
</ul>
|
||
<p><strong>🧠 Anyone Curious About ML Systems</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Understand how PyTorch, TensorFlow actually work</p></li>
|
||
<li><p>Build intuition for ML systems design and optimization</p></li>
|
||
<li><p>Appreciate the engineering behind modern AI breakthroughs</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="prerequisites">
|
||
<h3>Prerequisites<a class="headerlink" href="#prerequisites" title="Link to this heading">#</a></h3>
|
||
<p><strong>Required:</strong></p>
|
||
<ul class="simple">
|
||
<li><p><strong>Python Programming</strong>: Comfortable with classes, functions, basic NumPy</p></li>
|
||
<li><p><strong>Linear Algebra Basics</strong>: Matrix multiplication, gradients (we review as needed)</p></li>
|
||
<li><p><strong>Learning Mindset</strong>: Willingness to implement rather than just use</p></li>
|
||
</ul>
|
||
<p><strong>Not Required:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Prior ML framework experience (we build our own!)</p></li>
|
||
<li><p>Deep learning theory (we learn through implementation)</p></li>
|
||
<li><p>Advanced math (we focus on practical systems implementation)</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="what-youll-achieve-tier-by-tier-mastery">
|
||
<h2>What You’ll Achieve: Tier-by-Tier Mastery<a class="headerlink" href="#what-youll-achieve-tier-by-tier-mastery" title="Link to this heading">#</a></h2>
|
||
<section id="after-foundation-tier-modules-01-07">
|
||
<h3>After Foundation Tier (Modules 01-07)<a class="headerlink" href="#after-foundation-tier-modules-01-07" title="Link to this heading">#</a></h3>
|
||
<p>Build a complete neural network framework from mathematical first principles:</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># YOUR implementation training real networks on real data</span>
|
||
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">([</span>
|
||
<span class="n">Linear</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="c1"># Your linear algebra implementation</span>
|
||
<span class="n">ReLU</span><span class="p">(),</span> <span class="c1"># Your activation function</span>
|
||
<span class="n">Linear</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">64</span><span class="p">),</span> <span class="c1"># Your gradient-aware layers</span>
|
||
<span class="n">ReLU</span><span class="p">(),</span> <span class="c1"># Your nonlinearity</span>
|
||
<span class="n">Linear</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="c1"># Your classification head</span>
|
||
<span class="p">])</span>
|
||
|
||
<span class="c1"># YOUR complete training system</span>
|
||
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.001</span><span class="p">)</span> <span class="c1"># Your optimization algorithm</span>
|
||
<span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">dataloader</span><span class="p">:</span> <span class="c1"># Your data management</span>
|
||
<span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">x</span><span class="p">)</span> <span class="c1"># Your forward computation</span>
|
||
<span class="n">loss</span> <span class="o">=</span> <span class="n">CrossEntropyLoss</span><span class="p">()(</span><span class="n">output</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">y</span><span class="p">)</span> <span class="c1"># Your loss calculation</span>
|
||
<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> <span class="c1"># YOUR backpropagation engine</span>
|
||
<span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span> <span class="c1"># Your parameter updates</span>
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>🎯 Foundation Achievement</strong>: 95%+ accuracy on MNIST using 100% your own mathematical implementations</p>
|
||
</section>
|
||
<section id="after-architecture-tier-modules-08-13">
|
||
<h3>After Architecture Tier (Modules 08-13)<a class="headerlink" href="#after-architecture-tier-modules-08-13" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong>Computer Vision Mastery</strong>: CNNs achieving 75%+ accuracy on CIFAR-10 with YOUR convolution implementations</p></li>
|
||
<li><p><strong>Language Understanding</strong>: Transformers generating coherent text using YOUR attention mechanisms</p></li>
|
||
<li><p><strong>Universal Architecture</strong>: Discover why the SAME mathematical principles work for vision AND language</p></li>
|
||
<li><p><strong>AI Breakthrough Recreation</strong>: Implement the architectures that created the modern AI revolution</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="after-optimization-tier-modules-14-20">
|
||
<h3>After Optimization Tier (Modules 14-20)<a class="headerlink" href="#after-optimization-tier-modules-14-20" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong>Production Performance</strong>: Systems optimized for <100ms inference latency using YOUR profiling tools</p></li>
|
||
<li><p><strong>Memory Efficiency</strong>: Models compressed to 25% original size with YOUR quantization implementations</p></li>
|
||
<li><p><strong>Hardware Acceleration</strong>: Kernels achieving 10x speedups through YOUR vectorization techniques</p></li>
|
||
<li><p><strong>Competition Ready</strong>: Torch Olympics submissions competitive with industry implementations</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="the-ml-evolution-story-youll-experience">
|
||
<h2>The ML Evolution Story You’ll Experience<a class="headerlink" href="#the-ml-evolution-story-youll-experience" title="Link to this heading">#</a></h2>
|
||
<p>TinyTorch’s three-tier structure follows the actual historical progression of machine learning breakthroughs:</p>
|
||
<section id="foundation-era-1980s-1990s-foundation-tier">
|
||
<h3>Foundation Era (1980s-1990s) → Foundation Tier<a class="headerlink" href="#foundation-era-1980s-1990s-foundation-tier" title="Link to this heading">#</a></h3>
|
||
<p><strong>The Beginning</strong>: Mathematical foundations that started it all</p>
|
||
<ul class="simple">
|
||
<li><p><strong>1986 Breakthrough</strong>: Backpropagation enables multi-layer networks</p></li>
|
||
<li><p><strong>Your Implementation</strong>: Build automatic differentiation and gradient-based optimization</p></li>
|
||
<li><p><strong>Historical Milestone</strong>: Train MLPs to 95%+ accuracy on MNIST using YOUR autograd engine</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="architecture-era-1990s-2010s-architecture-tier">
|
||
<h3>Architecture Era (1990s-2010s) → Architecture Tier<a class="headerlink" href="#architecture-era-1990s-2010s-architecture-tier" title="Link to this heading">#</a></h3>
|
||
<p><strong>The Revolution</strong>: Specialized architectures for vision and language</p>
|
||
<ul class="simple">
|
||
<li><p><strong>1998 Breakthrough</strong>: CNNs revolutionize computer vision (LeCun’s LeNet)</p></li>
|
||
<li><p><strong>2017 Breakthrough</strong>: Transformers unify vision and language (“Attention is All You Need”)</p></li>
|
||
<li><p><strong>Your Implementation</strong>: Build CNNs achieving 75%+ on CIFAR-10, then transformers for text generation</p></li>
|
||
<li><p><strong>Historical Milestone</strong>: Recreate both revolutions using YOUR spatial and attention implementations</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="optimization-era-2010s-present-optimization-tier">
|
||
<h3>Optimization Era (2010s-Present) → Optimization Tier<a class="headerlink" href="#optimization-era-2010s-present-optimization-tier" title="Link to this heading">#</a></h3>
|
||
<p><strong>The Engineering</strong>: Production systems that scale to billions of users</p>
|
||
<ul class="simple">
|
||
<li><p><strong>2020s Breakthrough</strong>: Efficient inference enables real-time LLMs (GPT, ChatGPT)</p></li>
|
||
<li><p><strong>Your Implementation</strong>: Build KV-caching, quantization, and production optimizations</p></li>
|
||
<li><p><strong>Historical Milestone</strong>: Deploy systems competitive in Torch Olympics benchmarks</p></li>
|
||
</ul>
|
||
<p><strong>Why This Progression Matters</strong>: You’ll understand not just modern AI, but WHY it evolved this way. Each tier builds essential capabilities that inform the next, just like ML history itself.</p>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="systems-engineering-focus-why-tiers-matter">
|
||
<h2>Systems Engineering Focus: Why Tiers Matter<a class="headerlink" href="#systems-engineering-focus-why-tiers-matter" title="Link to this heading">#</a></h2>
|
||
<p>Traditional ML courses teach algorithms in isolation. TinyTorch’s tier structure teaches <strong>systems thinking</strong> - how components interact to create production ML systems.</p>
|
||
<section id="traditional-linear-approach">
|
||
<h3>Traditional Linear Approach:<a class="headerlink" href="#traditional-linear-approach" title="Link to this heading">#</a></h3>
|
||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>Module 1: Tensors → Module 2: Layers → Module 3: Training → ...
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>Problem</strong>: Students learn components but miss system interactions</p>
|
||
</section>
|
||
<section id="tinytorch-tier-approach">
|
||
<h3>TinyTorch Tier Approach:<a class="headerlink" href="#tinytorch-tier-approach" title="Link to this heading">#</a></h3>
|
||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>🏗️ Foundation Tier: Build mathematical infrastructure
|
||
🏛️ Architecture Tier: Compose intelligent architectures
|
||
⚡ Optimization Tier: Deploy at production scale
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>Advantage</strong>: Each tier builds complete, working systems with clear progression</p>
|
||
</section>
|
||
<section id="what-traditional-courses-teach-vs-tinytorch-tiers">
|
||
<h3>What Traditional Courses Teach vs. TinyTorch Tiers:<a class="headerlink" href="#what-traditional-courses-teach-vs-tinytorch-tiers" title="Link to this heading">#</a></h3>
|
||
<p><strong>Traditional</strong>: “Use <code class="docutils literal notranslate"><span class="pre">torch.optim.Adam</span></code> for optimization”
|
||
<strong>Foundation Tier</strong>: “Why Adam needs 3× more memory than SGD and how to implement both from mathematical first principles”</p>
|
||
<p><strong>Traditional</strong>: “Transformers use attention mechanisms”
|
||
<strong>Architecture Tier</strong>: “How attention creates O(N²) scaling, why this limits context windows, and how to implement efficient attention yourself”</p>
|
||
<p><strong>Traditional</strong>: “Deploy models with TensorFlow Serving”
|
||
<strong>Optimization Tier</strong>: “How to profile bottlenecks, implement KV-caching for 10× speedup, and compete in production benchmarks”</p>
|
||
</section>
|
||
<section id="career-impact-by-tier">
|
||
<h3>Career Impact by Tier<a class="headerlink" href="#career-impact-by-tier" title="Link to this heading">#</a></h3>
|
||
<p>After each tier, you become the team member who:</p>
|
||
<p><strong>🏗️ Foundation Tier Graduate</strong>:</p>
|
||
<ul class="simple">
|
||
<li><p>Debugs gradient flow issues: “Your ReLU is causing dead neurons”</p></li>
|
||
<li><p>Implements custom optimizers: “I’ll build a variant of Adam for this use case”</p></li>
|
||
<li><p>Understands memory patterns: “Batch size 64 hits your GPU memory limit here”</p></li>
|
||
</ul>
|
||
<p><strong>🏛️ Architecture Tier Graduate</strong>:</p>
|
||
<ul class="simple">
|
||
<li><p>Designs novel architectures: “We can adapt transformers for this computer vision task”</p></li>
|
||
<li><p>Optimizes attention patterns: “This attention bottleneck is why your model won’t scale to longer sequences”</p></li>
|
||
<li><p>Bridges vision and language: “The same mathematical principles work for both domains”</p></li>
|
||
</ul>
|
||
<p><strong>⚡ Optimization Tier Graduate</strong>:</p>
|
||
<ul class="simple">
|
||
<li><p>Deploys production systems: “I can get us from 500ms to 50ms inference latency”</p></li>
|
||
<li><p>Leads performance optimization: “Here’s our memory bottleneck and my 3-step plan to fix it”</p></li>
|
||
<li><p>Competes at industry scale: “Our optimizations achieve Torch Olympics benchmark performance”</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="learning-support-community">
|
||
<h2>Learning Support & Community<a class="headerlink" href="#learning-support-community" title="Link to this heading">#</a></h2>
|
||
<section id="comprehensive-infrastructure">
|
||
<h3>Comprehensive Infrastructure<a class="headerlink" href="#comprehensive-infrastructure" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong>Automated Testing</strong>: Every component includes comprehensive test suites</p></li>
|
||
<li><p><strong>Progress Tracking</strong>: 16-checkpoint capability assessment system</p></li>
|
||
<li><p><strong>CLI Tools</strong>: <code class="docutils literal notranslate"><span class="pre">tito</span></code> command-line interface for development workflow</p></li>
|
||
<li><p><strong>Visual Progress</strong>: Real-time tracking of learning milestones</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="multiple-learning-paths">
|
||
<h3>Multiple Learning Paths<a class="headerlink" href="#multiple-learning-paths" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong>Quick Exploration</strong> (5 min): Browser-based exploration, no setup required</p></li>
|
||
<li><p><strong>Serious Development</strong> (8+ weeks): Full local development environment</p></li>
|
||
<li><p><strong>Classroom Use</strong>: Complete course infrastructure with automated grading</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="professional-development-practices">
|
||
<h3>Professional Development Practices<a class="headerlink" href="#professional-development-practices" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p><strong>Version Control</strong>: Git-based workflow with feature branches</p></li>
|
||
<li><p><strong>Testing Culture</strong>: Test-driven development for all implementations</p></li>
|
||
<li><p><strong>Code Quality</strong>: Professional coding standards and review processes</p></li>
|
||
<li><p><strong>Documentation</strong>: Comprehensive guides and system architecture documentation</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="start-your-journey">
|
||
<h2>Start Your Journey<a class="headerlink" href="#start-your-journey" title="Link 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 Building ML Systems</h3>
|
||
<p style="margin: 0 0 1.5rem 0; color: #6c757d;">Choose your starting point based on your goals and time commitment</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;">15-Minute Start →</a>
|
||
<a href="01-setup.html" style="display: inline-block; background: #28a745; color: white; padding: 0.75rem 1.5rem; border-radius: 0.25rem; text-decoration: none; font-weight: 500;">Foundation Tier →</a>
|
||
</div>
|
||
<p><strong>Next Steps</strong>:</p>
|
||
<ul class="simple">
|
||
<li><p><strong>New to TinyTorch</strong>: Start with <span class="xref myst">Quick Start Guide</span> for immediate hands-on experience</p></li>
|
||
<li><p><strong>Ready to Commit</strong>: Begin <a class="reference internal" href="../modules/01_tensor_ABOUT.html"><span class="std std-doc">Module 01: Tensor</span></a> to start building</p></li>
|
||
<li><p><strong>Teaching a Course</strong>: Review <a class="reference internal" href="#../getting-started.html#instructors"><span class="xref myst">Getting Started Guide - For Instructors</span></a> for classroom integration</p></li>
|
||
</ul>
|
||
<div class="tip admonition">
|
||
<p class="admonition-title">Your Three-Tier Journey Awaits</p>
|
||
<p>By completing all three tiers, you’ll have built a complete ML framework that rivals production implementations:</p>
|
||
<p><strong>🏗️ Foundation Tier Achievement</strong>: 95%+ accuracy on MNIST with YOUR mathematical implementations
|
||
<strong>🏛️ Architecture Tier Achievement</strong>: 75%+ accuracy on CIFAR-10 AND coherent text generation
|
||
<strong>⚡ Optimization Tier Achievement</strong>: Production systems competitive in Torch Olympics benchmarks</p>
|
||
<p>All using code you wrote yourself, from mathematical first principles to production optimization.</p>
|
||
</div>
|
||
<p><strong>📖 Want to understand the pedagogical narrative behind this structure?</strong> See <a class="reference internal" href="learning-journey.html"><span class="std std-doc">The Learning Journey</span></a> to understand WHY modules flow this way and HOW they build on each other through a six-act learning story.</p>
|
||
<hr class="docutils" />
|
||
<section id="foundation-tier-modules-01-07">
|
||
<h3>Foundation Tier (Modules 01-07)<a class="headerlink" href="#foundation-tier-modules-01-07" title="Link to this heading">#</a></h3>
|
||
<p><strong>Building Blocks of ML Systems • 6-8 weeks • All Prerequisites for Neural Networks</strong></p>
|
||
<div style="background: #f8f9fd; border: 1px solid #e0e7ff; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0;">
|
||
<p><strong>What You’ll Learn</strong>: Build the mathematical and computational infrastructure that powers all neural networks. Master tensor operations, gradient computation, and optimization algorithms.</p>
|
||
<p><strong>Prerequisites</strong>: Python programming, basic linear algebra (matrix multiplication)</p>
|
||
<p><strong>Career Connection</strong>: Foundation skills required for ML Infrastructure Engineer, Research Engineer, Framework Developer roles</p>
|
||
<p><strong>Time Investment</strong>: ~20 hours total (3 hours/week for 6-8 weeks)</p>
|
||
</div>
|
||
<div class="pst-scrollable-table-container"><table class="table">
|
||
<thead>
|
||
<tr class="row-odd"><th class="head"><p>Module</p></th>
|
||
<th class="head"><p>Component</p></th>
|
||
<th class="head"><p>Core Capability</p></th>
|
||
<th class="head"><p>Real-World Connection</p></th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="row-even"><td><p><strong>01</strong></p></td>
|
||
<td><p><strong>Tensor</strong></p></td>
|
||
<td><p>Data structures and operations</p></td>
|
||
<td><p>NumPy, PyTorch tensors</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td><p><strong>02</strong></p></td>
|
||
<td><p><strong>Activations</strong></p></td>
|
||
<td><p>Nonlinear functions</p></td>
|
||
<td><p>ReLU, attention activations</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td><p><strong>03</strong></p></td>
|
||
<td><p><strong>Layers</strong></p></td>
|
||
<td><p>Linear transformations</p></td>
|
||
<td><p><code class="docutils literal notranslate"><span class="pre">nn.Linear</span></code>, dense layers</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td><p><strong>04</strong></p></td>
|
||
<td><p><strong>Losses</strong></p></td>
|
||
<td><p>Optimization objectives</p></td>
|
||
<td><p>CrossEntropy, MSE loss</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td><p><strong>05</strong></p></td>
|
||
<td><p><strong>Autograd</strong></p></td>
|
||
<td><p>Automatic differentiation</p></td>
|
||
<td><p>PyTorch autograd engine</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td><p><strong>06</strong></p></td>
|
||
<td><p><strong>Optimizers</strong></p></td>
|
||
<td><p>Parameter updates</p></td>
|
||
<td><p>Adam, SGD optimizers</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td><p><strong>07</strong></p></td>
|
||
<td><p><strong>Training</strong></p></td>
|
||
<td><p>Complete training loops</p></td>
|
||
<td><p>Model.fit(), training scripts</p></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
<p><strong>🎯 Tier Milestone</strong>: Train neural networks achieving <strong>95%+ accuracy on MNIST</strong> using 100% your own implementations!</p>
|
||
<p><strong>Skills Gained</strong>:</p>
|
||
<ul class="simple">
|
||
<li><p>Understand memory layout and computational graphs</p></li>
|
||
<li><p>Debug gradient flow and numerical stability issues</p></li>
|
||
<li><p>Implement any optimization algorithm from research papers</p></li>
|
||
<li><p>Build custom neural network architectures from scratch</p></li>
|
||
</ul>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="architecture-tier-modules-08-13">
|
||
<h3>Architecture Tier (Modules 08-13)<a class="headerlink" href="#architecture-tier-modules-08-13" title="Link to this heading">#</a></h3>
|
||
<p><strong>Modern AI Algorithms • 4-6 weeks • Vision + Language Architectures</strong></p>
|
||
<div style="background: #fef7ff; border: 1px solid #f3e8ff; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0;">
|
||
<p><strong>What You’ll Learn</strong>: Implement the architectures powering modern AI: convolutional networks for vision and transformers for language. Discover why the same mathematical principles work across domains.</p>
|
||
<p><strong>Prerequisites</strong>: Foundation Tier complete (Modules 01-07)</p>
|
||
<p><strong>Career Connection</strong>: Computer Vision Engineer, NLP Engineer, AI Research Scientist, ML Product Manager roles</p>
|
||
<p><strong>Time Investment</strong>: ~25 hours total (4-6 hours/week for 4-6 weeks)</p>
|
||
</div>
|
||
<div class="pst-scrollable-table-container"><table class="table">
|
||
<thead>
|
||
<tr class="row-odd"><th class="head"><p>Module</p></th>
|
||
<th class="head"><p>Component</p></th>
|
||
<th class="head"><p>Core Capability</p></th>
|
||
<th class="head"><p>Real-World Connection</p></th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="row-even"><td><p><strong>08</strong></p></td>
|
||
<td><p><strong>Spatial</strong></p></td>
|
||
<td><p>Convolutions and regularization</p></td>
|
||
<td><p>CNNs, ResNet, computer vision</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td><p><strong>09</strong></p></td>
|
||
<td><p><strong>DataLoader</strong></p></td>
|
||
<td><p>Batch processing</p></td>
|
||
<td><p>PyTorch DataLoader, tf.data</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td><p><strong>10</strong></p></td>
|
||
<td><p><strong>Tokenization</strong></p></td>
|
||
<td><p>Text preprocessing</p></td>
|
||
<td><p>BERT tokenizer, GPT tokenizer</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td><p><strong>11</strong></p></td>
|
||
<td><p><strong>Embeddings</strong></p></td>
|
||
<td><p>Representation learning</p></td>
|
||
<td><p>Word2Vec, positional encodings</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td><p><strong>12</strong></p></td>
|
||
<td><p><strong>Attention</strong></p></td>
|
||
<td><p>Information routing</p></td>
|
||
<td><p>Multi-head attention, self-attention</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td><p><strong>13</strong></p></td>
|
||
<td><p><strong>Transformers</strong></p></td>
|
||
<td><p>Modern architectures</p></td>
|
||
<td><p>GPT, BERT, Vision Transformer</p></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
<p><strong>🎯 Tier Milestone</strong>: Achieve <strong>75%+ accuracy on CIFAR-10</strong> with CNNs AND generate coherent text with transformers!</p>
|
||
<p><strong>Skills Gained</strong>:</p>
|
||
<ul class="simple">
|
||
<li><p>Understand why convolution works for spatial data</p></li>
|
||
<li><p>Implement attention mechanisms from scratch</p></li>
|
||
<li><p>Build transformer architectures for any domain</p></li>
|
||
<li><p>Debug sequence modeling and attention patterns</p></li>
|
||
</ul>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="optimization-tier-modules-14-19">
|
||
<h3>Optimization Tier (Modules 14-19)<a class="headerlink" href="#optimization-tier-modules-14-19" title="Link to this heading">#</a></h3>
|
||
<p><strong>Production & Performance • 4-6 weeks • Deploy and Scale ML Systems</strong></p>
|
||
<div style="background: #f0fdfa; border: 1px solid #a7f3d0; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0;">
|
||
<p><strong>What You’ll Learn</strong>: Transform research models into production systems. Master profiling, optimization, and deployment techniques used by companies like OpenAI, Google, and Meta.</p>
|
||
<p><strong>Prerequisites</strong>: Architecture Tier complete (Modules 08-13)</p>
|
||
<p><strong>Career Connection</strong>: ML Systems Engineer, Performance Engineer, MLOps Engineer, Senior ML Engineer roles</p>
|
||
<p><strong>Time Investment</strong>: ~30 hours total (5-7 hours/week for 4-6 weeks)</p>
|
||
</div>
|
||
<div class="pst-scrollable-table-container"><table class="table">
|
||
<thead>
|
||
<tr class="row-odd"><th class="head"><p>Module</p></th>
|
||
<th class="head"><p>Component</p></th>
|
||
<th class="head"><p>Core Capability</p></th>
|
||
<th class="head"><p>Real-World Connection</p></th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="row-even"><td><p><strong>14</strong></p></td>
|
||
<td><p><strong>Profiling</strong></p></td>
|
||
<td><p>Performance analysis</p></td>
|
||
<td><p>PyTorch Profiler, TensorBoard</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td><p><strong>15</strong></p></td>
|
||
<td><p><strong>Quantization</strong></p></td>
|
||
<td><p>Memory efficiency</p></td>
|
||
<td><p>INT8 inference, model compression</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td><p><strong>16</strong></p></td>
|
||
<td><p><strong>Compression</strong></p></td>
|
||
<td><p>Model optimization</p></td>
|
||
<td><p>Pruning, distillation, ONNX</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td><p><strong>17</strong></p></td>
|
||
<td><p><strong>Memoization</strong></p></td>
|
||
<td><p>Memory management</p></td>
|
||
<td><p>KV-cache for generation</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td><p><strong>18</strong></p></td>
|
||
<td><p><strong>Acceleration</strong></p></td>
|
||
<td><p>Speed improvements</p></td>
|
||
<td><p>CUDA kernels, vectorization</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td><p><strong>19</strong></p></td>
|
||
<td><p><strong>Benchmarking</strong></p></td>
|
||
<td><p>Measurement systems</p></td>
|
||
<td><p>Torch Olympics, production monitoring</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td><p><strong>20</strong></p></td>
|
||
<td><p><strong>Capstone</strong></p></td>
|
||
<td><p>Full system integration</p></td>
|
||
<td><p>End-to-end ML pipeline</p></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
<p><strong>🎯 Tier Milestone</strong>: Build <strong>production-ready systems</strong> competitive in Torch Olympics benchmarks!</p>
|
||
<p><strong>Skills Gained</strong>:</p>
|
||
<ul class="simple">
|
||
<li><p>Profile memory usage and identify bottlenecks</p></li>
|
||
<li><p>Implement efficient inference optimizations</p></li>
|
||
<li><p>Deploy models with <100ms latency requirements</p></li>
|
||
<li><p>Design scalable ML system architectures</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="learning-path-recommendations">
|
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<h2>Learning Path Recommendations<a class="headerlink" href="#learning-path-recommendations" title="Link to this heading">#</a></h2>
|
||
<section id="choose-your-learning-style">
|
||
<h3>Choose Your Learning Style<a class="headerlink" href="#choose-your-learning-style" title="Link to this heading">#</a></h3>
|
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<div style="display: grid; grid-template-columns: repeat(3, 1fr); gap: 1.5rem; margin: 2rem 0;">
|
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<div style="background: #fff7ed; border: 1px solid #fdba74; padding: 1.5rem; border-radius: 0.5rem;">
|
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<h4 style="margin: 0 0 1rem 0; color: #c2410c;">🚀 Complete Builder</h4>
|
||
<p style="margin: 0 0 1rem 0; font-size: 0.9rem;">Implement every component from scratch</p>
|
||
<p style="margin: 0; font-size: 0.85rem; color: #6b7280;"><strong>Time:</strong> 14-18 weeks<br><strong>Ideal for:</strong> CS students, aspiring ML engineers</p>
|
||
</div>
|
||
<div style="background: #f0f9ff; border: 1px solid #7dd3fc; padding: 1.5rem; border-radius: 0.5rem;">
|
||
<h4 style="margin: 0 0 1rem 0; color: #0284c7;">⚡ Focused Explorer</h4>
|
||
<p style="margin: 0 0 1rem 0; font-size: 0.9rem;">Pick one tier based on your goals</p>
|
||
<p style="margin: 0; font-size: 0.85rem; color: #6b7280;"><strong>Time:</strong> 4-8 weeks<br><strong>Ideal for:</strong> Working professionals, specific skill gaps</p>
|
||
</div>
|
||
<div style="background: #f0fdf4; border: 1px solid #86efac; padding: 1.5rem; border-radius: 0.5rem;">
|
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<h4 style="margin: 0 0 1rem 0; color: #166534;">📚 Guided Learner</h4>
|
||
<p style="margin: 0 0 1rem 0; font-size: 0.9rem;">Study implementations with hands-on exercises</p>
|
||
<p style="margin: 0; font-size: 0.85rem; color: #6b7280;"><strong>Time:</strong> 8-12 weeks<br><strong>Ideal for:</strong> Self-directed learners, bootcamp graduates</p>
|
||
</div>
|
||
</div>
|
||
<hr class="docutils" />
|
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<p>Welcome to ML systems engineering!</p>
|
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</section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#the-origin-story-why-tinytorch-exists">The Origin Story: Why TinyTorch Exists</a><ul class="nav section-nav flex-column">
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