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<h1>Prerequisites & Self-Assessment</h1>
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<div id="jb-print-toc">
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<h2> Contents </h2>
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</div>
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<nav aria-label="Page">
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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="#core-requirements">Core Requirements</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#python-programming">1. Python Programming</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#basic-linear-algebra">2. Basic Linear Algebra</a></li>
|
||
</ul>
|
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</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#nice-to-have-background">“Nice to Have” Background</a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#self-assessment-which-learning-path-fits-you">Self-Assessment: Which Learning Path Fits You?</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="#path-a-foundation-first-builder-recommended-for-most">Path A: Foundation-First Builder (Recommended for most)</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#path-b-focused-systems-engineer">Path B: Focused Systems Engineer</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#path-c-academic-researcher">Path C: Academic Researcher</a></li>
|
||
</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="#complementary-learning-resources">Complementary Learning Resources</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="#essential-systems-context">Essential Systems Context</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#mathematical-foundations">Mathematical Foundations</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#visual-intuition">Visual Intuition</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#python-numpy">Python & NumPy</a></li>
|
||
</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-begin">Ready to Begin?</a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#next-steps">Next Steps</a></li>
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</ul>
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</nav>
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</div>
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</div>
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<div id="searchbox"></div>
|
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<article class="bd-article">
|
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<section id="prerequisites-self-assessment">
|
||
<h1>Prerequisites & Self-Assessment<a class="headerlink" href="#prerequisites-self-assessment" title="Link to this heading">#</a></h1>
|
||
<p><strong>Purpose</strong>: Ensure you have the foundational knowledge to succeed in TinyTorch and discover complementary resources for deeper learning.</p>
|
||
<hr class="docutils" />
|
||
<section id="core-requirements">
|
||
<h2>Core Requirements<a class="headerlink" href="#core-requirements" title="Link to this heading">#</a></h2>
|
||
<p>You need TWO things to start building:</p>
|
||
<section id="python-programming">
|
||
<h3>1. Python Programming<a class="headerlink" href="#python-programming" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Comfortable writing functions and classes</p></li>
|
||
<li><p>Familiarity with basic NumPy arrays</p></li>
|
||
<li><p>No ML framework experience required—you’ll build your own!</p></li>
|
||
</ul>
|
||
<p><strong>Self-check</strong>: Can you write a Python class with <code class="docutils literal notranslate"><span class="pre">__init__</span></code> and methods?</p>
|
||
</section>
|
||
<section id="basic-linear-algebra">
|
||
<h3>2. Basic Linear Algebra<a class="headerlink" href="#basic-linear-algebra" title="Link to this heading">#</a></h3>
|
||
<ul class="simple">
|
||
<li><p>Understand matrix multiplication conceptually</p></li>
|
||
<li><p>Know what a gradient (derivative) represents at a high level</p></li>
|
||
</ul>
|
||
<p><strong>Self-check</strong>: Do you know what multiplying two matrices means?</p>
|
||
<p><strong>That’s it. You’re ready to start building.</strong></p>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="nice-to-have-background">
|
||
<h2>“Nice to Have” Background<a class="headerlink" href="#nice-to-have-background" title="Link to this heading">#</a></h2>
|
||
<p><strong>We teach these concepts as you build</strong>—you don’t need them upfront:</p>
|
||
<ul class="simple">
|
||
<li><p><strong>Calculus (derivatives)</strong>: Module 05 (Autograd) teaches this through implementation</p></li>
|
||
<li><p><strong>Deep learning theory</strong>: You’ll learn by building, not lectures</p></li>
|
||
<li><p><strong>Advanced NumPy</strong>: We introduce operations as needed in each module</p></li>
|
||
</ul>
|
||
<p><strong>Learning Philosophy</strong>: TinyTorch teaches ML systems through implementation. You’ll understand backpropagation by building it, not by watching lectures about it.</p>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="self-assessment-which-learning-path-fits-you">
|
||
<h2>Self-Assessment: Which Learning Path Fits You?<a class="headerlink" href="#self-assessment-which-learning-path-fits-you" title="Link to this heading">#</a></h2>
|
||
<section id="path-a-foundation-first-builder-recommended-for-most">
|
||
<h3>Path A: Foundation-First Builder (Recommended for most)<a class="headerlink" href="#path-a-foundation-first-builder-recommended-for-most" title="Link to this heading">#</a></h3>
|
||
<p><strong>You are:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Strong Python programmer</p></li>
|
||
<li><p>Curious about ML systems</p></li>
|
||
<li><p>Want to understand how frameworks work</p></li>
|
||
</ul>
|
||
<p><strong>Start with</strong>: Module 01 (Tensor)</p>
|
||
<p><strong>Best for</strong>: CS students, software engineers transitioning to ML, anyone wanting deep systems understanding</p>
|
||
</section>
|
||
<section id="path-b-focused-systems-engineer">
|
||
<h3>Path B: Focused Systems Engineer<a class="headerlink" href="#path-b-focused-systems-engineer" title="Link to this heading">#</a></h3>
|
||
<p><strong>You are:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Professional ML engineer</p></li>
|
||
<li><p>Need specific optimization skills</p></li>
|
||
<li><p>Want production deployment knowledge</p></li>
|
||
</ul>
|
||
<p><strong>Start with</strong>: Review Foundation Tier (01-07), focus on Optimization Tier (14-19)</p>
|
||
<p><strong>Best for</strong>: Working engineers debugging production systems, performance optimization specialists</p>
|
||
</section>
|
||
<section id="path-c-academic-researcher">
|
||
<h3>Path C: Academic Researcher<a class="headerlink" href="#path-c-academic-researcher" title="Link to this heading">#</a></h3>
|
||
<p><strong>You are:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>ML theory background</p></li>
|
||
<li><p>Need implementation skills</p></li>
|
||
<li><p>Want to prototype novel architectures</p></li>
|
||
</ul>
|
||
<p><strong>Start with</strong>: Module 01, accelerate through familiar concepts</p>
|
||
<p><strong>Best for</strong>: PhD students, research engineers, anyone implementing custom operations</p>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="complementary-learning-resources">
|
||
<h2>Complementary Learning Resources<a class="headerlink" href="#complementary-learning-resources" title="Link to this heading">#</a></h2>
|
||
<section id="essential-systems-context">
|
||
<h3>Essential Systems Context<a class="headerlink" href="#essential-systems-context" title="Link to this heading">#</a></h3>
|
||
<p><strong><a class="reference external" href="https://mlsysbook.ai">Machine Learning Systems</a></strong> by Prof. Vijay Janapa Reddi (Harvard)</p>
|
||
<ul class="simple">
|
||
<li><p>TinyTorch’s companion textbook providing systems perspective</p></li>
|
||
<li><p>Covers production ML engineering, hardware acceleration, deployment</p></li>
|
||
<li><p><strong>Perfect pairing</strong>: TinyTorch teaches implementation, ML Systems book teaches context</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="mathematical-foundations">
|
||
<h3>Mathematical Foundations<a class="headerlink" href="#mathematical-foundations" title="Link to this heading">#</a></h3>
|
||
<p><strong><a class="reference external" href="https://www.deeplearningbook.org/">Deep Learning Book</a></strong> by Goodfellow, Bengio, Courville</p>
|
||
<ul class="simple">
|
||
<li><p>Comprehensive theoretical foundations</p></li>
|
||
<li><p>Mathematical background for concepts you’ll implement</p></li>
|
||
<li><p><strong>Use alongside TinyTorch</strong> for deeper understanding</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="visual-intuition">
|
||
<h3>Visual Intuition<a class="headerlink" href="#visual-intuition" title="Link to this heading">#</a></h3>
|
||
<p><strong><a class="reference external" href="https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi">3Blue1Brown: Neural Networks</a></strong></p>
|
||
<ul class="simple">
|
||
<li><p>Visual explanations of backpropagation, gradient descent, neural networks</p></li>
|
||
<li><p><strong>Perfect visual complement</strong> to TinyTorch’s hands-on implementation</p></li>
|
||
</ul>
|
||
<p><strong><a class="reference external" href="https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab">3Blue1Brown: Linear Algebra</a></strong></p>
|
||
<ul class="simple">
|
||
<li><p>Geometric intuition for vectors, matrices, transformations</p></li>
|
||
<li><p><strong>Helpful refresher</strong> for tensor operations and matrix multiplication</p></li>
|
||
</ul>
|
||
</section>
|
||
<section id="python-numpy">
|
||
<h3>Python & NumPy<a class="headerlink" href="#python-numpy" title="Link to this heading">#</a></h3>
|
||
<p><strong><a class="reference external" href="https://numpy.org/doc/stable/user/quickstart.md">NumPy Quickstart Tutorial</a></strong></p>
|
||
<ul class="simple">
|
||
<li><p>Essential NumPy operations and array manipulation</p></li>
|
||
<li><p><strong>Review before Module 01</strong> if NumPy is unfamiliar</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="ready-to-begin">
|
||
<h2>Ready to Begin?<a class="headerlink" href="#ready-to-begin" title="Link to this heading">#</a></h2>
|
||
<p><strong>If you can:</strong></p>
|
||
<ol class="arabic simple">
|
||
<li><p>✅ Write a Python class with methods</p></li>
|
||
<li><p>✅ Explain what matrix multiplication does</p></li>
|
||
<li><p>✅ Debug Python code using print statements</p></li>
|
||
</ol>
|
||
<p><strong>Then you’re ready to start building!</strong></p>
|
||
<p><strong>Not quite there?</strong> Work through the resources above, then return when ready. TinyTorch will still be here, and you’ll get more value once foundations are solid.</p>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="next-steps">
|
||
<h2>Next Steps<a class="headerlink" href="#next-steps" title="Link to this heading">#</a></h2>
|
||
<p><strong>Ready to Build:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>See <span class="xref myst">Quick Start Guide</span> for hands-on experience</p></li>
|
||
<li><p>See <span class="xref myst">Student Workflow</span> for development process</p></li>
|
||
<li><p>See <a class="reference internal" href="chapters/00-introduction.html"><span class="std std-doc">Course Structure</span></a> for full curriculum</p></li>
|
||
</ul>
|
||
<p><strong>Need More Context:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>See <a class="reference internal" href="resources.html"><span class="std std-doc">Additional Resources</span></a> for broader ML learning materials</p></li>
|
||
<li><p>See <a class="reference internal" href="faq.html"><span class="std std-doc">FAQ</span></a> for common questions about TinyTorch</p></li>
|
||
<li><p>See <a class="reference internal" href="community.html"><span class="std std-doc">Community</span></a> to connect with other learners</p></li>
|
||
</ul>
|
||
<hr class="docutils" />
|
||
<p><strong>Your journey from ML user to ML systems engineer starts here.</strong></p>
|
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</section>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#python-programming">1. Python Programming</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#path-a-foundation-first-builder-recommended-for-most">Path A: Foundation-First Builder (Recommended for most)</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#path-b-focused-systems-engineer">Path B: Focused Systems Engineer</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#path-c-academic-researcher">Path C: Academic Researcher</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#complementary-learning-resources">Complementary Learning Resources</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="#python-numpy">Python & NumPy</a></li>
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