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Getting Started
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<div id="jb-print-docs-body" class="onlyprint">
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<h1>TinyTorch Datasets</h1>
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<!-- Table of contents -->
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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="#design-philosophy">Design Philosophy</a></li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#shipped-datasets-included-with-tinytorch">Shipped Datasets (Included with TinyTorch)</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#tinydigits-handwritten-digit-recognition">TinyDigits - Handwritten Digit Recognition</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#tinytalks-conversational-q-a-dataset">TinyTalks - Conversational Q&A Dataset</a></li>
|
||
</ul>
|
||
</li>
|
||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#downloaded-datasets-auto-downloaded-on-demand">Downloaded Datasets (Auto-Downloaded On-Demand)</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#mnist-handwritten-digit-classification">MNIST - Handwritten Digit Classification</a></li>
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#cifar-10-natural-image-classification">CIFAR-10 - Natural Image Classification</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="#dataset-selection-rationale">Dataset Selection Rationale</a><ul class="nav section-nav flex-column">
|
||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#why-these-specific-datasets">Why These Specific Datasets?</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="#accessing-datasets">Accessing Datasets</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="#for-students">For Students</a></li>
|
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#for-developers-researchers">For Developers/Researchers</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="#dataset-sizes-summary">Dataset Sizes Summary</a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#why-ship-with-repo-matters">Why Ship-with-Repo Matters</a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#frequently-asked-questions">Frequently Asked Questions</a></li>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#related-documentation">Related Documentation</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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|
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|
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<div id="searchbox"></div>
|
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<article class="bd-article">
|
||
|
||
<section id="tinytorch-datasets">
|
||
<h1>TinyTorch Datasets<a class="headerlink" href="#tinytorch-datasets" 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;">Ship-with-Repo Datasets for Fast Learning</h2>
|
||
<p style="margin: 0; font-size: 1.1rem; color: #6c757d;">Small datasets for instant iteration + standard benchmarks for validation</p>
|
||
</div>
|
||
<p><strong>Purpose</strong>: Understand TinyTorch’s dataset strategy and where to find each dataset used in milestones.</p>
|
||
<section id="design-philosophy">
|
||
<h2>Design Philosophy<a class="headerlink" href="#design-philosophy" title="Permalink to this heading">#</a></h2>
|
||
<p>TinyTorch uses a two-tier dataset approach:</p>
|
||
<div style="display: grid; grid-template-columns: 1fr 1fr; gap: 1.5rem; margin: 2rem 0;">
|
||
<div style="background: #e3f2fd; border: 1px solid #2196f3; padding: 1.5rem; border-radius: 0.5rem;">
|
||
<h3 style="margin: 0 0 1rem 0; color: #1976d2;">Shipped Datasets</h3>
|
||
<p style="margin: 0 0 1rem 0;"><strong>~350 KB total - Ships with repository</strong></p>
|
||
<ul style="margin: 0; font-size: 0.9rem;">
|
||
<li>Small enough to fit in Git (~1K samples each)</li>
|
||
<li>Fast training (seconds to minutes)</li>
|
||
<li>Instant gratification for learners</li>
|
||
<li>Works offline - no download needed</li>
|
||
<li>Perfect for rapid iteration</li>
|
||
</ul>
|
||
</div>
|
||
<div style="background: #f3e5f5; border: 1px solid #9c27b0; padding: 1.5rem; border-radius: 0.5rem;">
|
||
<h3 style="margin: 0 0 1rem 0; color: #7b1fa2;">Downloaded Datasets</h3>
|
||
<p style="margin: 0 0 1rem 0;"><strong>~180 MB - Auto-downloaded when needed</strong></p>
|
||
<ul style="margin: 0; font-size: 0.9rem;">
|
||
<li>Standard ML benchmarks (MNIST, CIFAR-10)</li>
|
||
<li>Larger scale (~60K samples)</li>
|
||
<li>Used for validation and scaling</li>
|
||
<li>Downloaded automatically by milestones</li>
|
||
<li>Cached locally for reuse</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
<p><strong>Philosophy</strong>: Following Andrej Karpathy’s “~1K samples” approach—small datasets for learning, full benchmarks for validation.</p>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="shipped-datasets-included-with-tinytorch">
|
||
<h2>Shipped Datasets (Included with TinyTorch)<a class="headerlink" href="#shipped-datasets-included-with-tinytorch" title="Permalink to this heading">#</a></h2>
|
||
<section id="tinydigits-handwritten-digit-recognition">
|
||
<h3>TinyDigits - Handwritten Digit Recognition<a class="headerlink" href="#tinydigits-handwritten-digit-recognition" title="Permalink to this heading">#</a></h3>
|
||
<div style="background: #fff5f5; border-left: 4px solid #e74c3c; padding: 1.5rem; margin: 1.5rem 0;">
|
||
<p><strong>Location</strong>: <code class="docutils literal notranslate"><span class="pre">datasets/tinydigits/</span></code><br />
|
||
<strong>Size</strong>: ~310 KB<br />
|
||
<strong>Used by</strong>: Milestones 03 & 04 (MLP and CNN examples)</p>
|
||
<p><strong>Contents:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>1,000 training samples</p></li>
|
||
<li><p>200 test samples</p></li>
|
||
<li><p>8×8 grayscale images (downsampled from MNIST)</p></li>
|
||
<li><p>10 classes (digits 0-9)</p></li>
|
||
</ul>
|
||
<p><strong>Format</strong>: Python pickle file with NumPy arrays</p>
|
||
<p><strong>Why 8×8?</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Fast iteration: Trains in seconds</p></li>
|
||
<li><p>Memory-friendly: Small enough to debug</p></li>
|
||
<li><p>Conceptually complete: Same challenges as 28×28 MNIST</p></li>
|
||
<li><p>Git-friendly: Only 310 KB vs 10 MB for full MNIST</p></li>
|
||
</ul>
|
||
<p><strong>Usage in milestones:</strong></p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Automatically loaded by milestones</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">datasets.tinydigits</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_tinydigits</span>
|
||
<span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">load_tinydigits</span><span class="p">()</span>
|
||
<span class="c1"># X_train shape: (1000, 8, 8)</span>
|
||
<span class="c1"># y_train shape: (1000,)</span>
|
||
</pre></div>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
<section id="tinytalks-conversational-q-a-dataset">
|
||
<h3>TinyTalks - Conversational Q&A Dataset<a class="headerlink" href="#tinytalks-conversational-q-a-dataset" title="Permalink to this heading">#</a></h3>
|
||
<div style="background: #f0fff4; border-left: 4px solid #22c55e; padding: 1.5rem; margin: 1.5rem 0;">
|
||
<p><strong>Location</strong>: <code class="docutils literal notranslate"><span class="pre">datasets/tinytalks/</span></code><br />
|
||
<strong>Size</strong>: ~40 KB<br />
|
||
<strong>Used by</strong>: Milestone 05 (Transformer/GPT text generation)</p>
|
||
<p><strong>Contents:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>350 Q&A pairs across 5 difficulty levels</p></li>
|
||
<li><p>Character-level text data</p></li>
|
||
<li><p>Topics: General knowledge, math, science, reasoning</p></li>
|
||
<li><p>Balanced difficulty distribution</p></li>
|
||
</ul>
|
||
<p><strong>Format</strong>: Plain text files with Q: / A: format</p>
|
||
<p><strong>Why conversational format?</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Engaging: Questions feel natural</p></li>
|
||
<li><p>Varied: Different answer lengths and complexity</p></li>
|
||
<li><p>Educational: Difficulty levels scaffold learning</p></li>
|
||
<li><p>Practical: Mirrors real chatbot use cases</p></li>
|
||
</ul>
|
||
<p><strong>Example:</strong></p>
|
||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>Q: What is the capital of France?
|
||
A: Paris
|
||
|
||
Q: If a train travels 120 km in 2 hours, what is its average speed?
|
||
A: 60 km/h
|
||
</pre></div>
|
||
</div>
|
||
<p><strong>Usage in milestones:</strong></p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Automatically loaded by transformer milestones</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">datasets.tinytalks</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_tinytalks</span>
|
||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">load_tinytalks</span><span class="p">()</span>
|
||
<span class="c1"># Returns list of (question, answer) pairs</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>See detailed documentation: <code class="docutils literal notranslate"><span class="pre">datasets/tinytalks/README.md</span></code></p>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="downloaded-datasets-auto-downloaded-on-demand">
|
||
<h2>Downloaded Datasets (Auto-Downloaded On-Demand)<a class="headerlink" href="#downloaded-datasets-auto-downloaded-on-demand" title="Permalink to this heading">#</a></h2>
|
||
<p>These standard benchmarks download automatically when you run relevant milestone scripts:</p>
|
||
<section id="mnist-handwritten-digit-classification">
|
||
<h3>MNIST - Handwritten Digit Classification<a class="headerlink" href="#mnist-handwritten-digit-classification" title="Permalink to this heading">#</a></h3>
|
||
<div style="background: #fffbeb; border-left: 4px solid #f59e0b; padding: 1.5rem; margin: 1.5rem 0;">
|
||
<p><strong>Downloads to</strong>: <code class="docutils literal notranslate"><span class="pre">milestones/datasets/mnist/</span></code><br />
|
||
<strong>Size</strong>: ~10 MB (compressed)<br />
|
||
<strong>Used by</strong>: <code class="docutils literal notranslate"><span class="pre">milestones/03_1986_mlp/02_rumelhart_mnist.py</span></code></p>
|
||
<p><strong>Contents:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>60,000 training samples</p></li>
|
||
<li><p>10,000 test samples</p></li>
|
||
<li><p>28×28 grayscale images</p></li>
|
||
<li><p>10 classes (digits 0-9)</p></li>
|
||
</ul>
|
||
<p><strong>Auto-download</strong>: When you run the MNIST milestone script, it automatically:</p>
|
||
<ol class="arabic simple">
|
||
<li><p>Checks if data exists locally</p></li>
|
||
<li><p>Downloads if needed (~10 MB)</p></li>
|
||
<li><p>Caches for future runs</p></li>
|
||
<li><p>Loads data using your TinyTorch DataLoader</p></li>
|
||
</ol>
|
||
<p><strong>Purpose</strong>: Validate that your framework achieves production-level results (95%+ accuracy target)</p>
|
||
<p><strong>Milestone goal</strong>: Implement backpropagation and achieve 95%+ accuracy—matching 1986 Rumelhart’s breakthrough.</p>
|
||
</div>
|
||
</section>
|
||
<section id="cifar-10-natural-image-classification">
|
||
<h3>CIFAR-10 - Natural Image Classification<a class="headerlink" href="#cifar-10-natural-image-classification" title="Permalink to this heading">#</a></h3>
|
||
<div style="background: #fdf2f8; border-left: 4px solid #ec4899; padding: 1.5rem; margin: 1.5rem 0;">
|
||
<p><strong>Downloads to</strong>: <code class="docutils literal notranslate"><span class="pre">milestones/datasets/cifar-10/</span></code><br />
|
||
<strong>Size</strong>: ~170 MB (compressed)<br />
|
||
<strong>Used by</strong>: <code class="docutils literal notranslate"><span class="pre">milestones/04_1998_cnn/02_lecun_cifar10.py</span></code></p>
|
||
<p><strong>Contents:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>50,000 training samples</p></li>
|
||
<li><p>10,000 test samples</p></li>
|
||
<li><p>32×32 RGB images</p></li>
|
||
<li><p>10 classes (airplane, car, bird, cat, deer, dog, frog, horse, ship, truck)</p></li>
|
||
</ul>
|
||
<p><strong>Auto-download</strong>: Milestone script handles everything:</p>
|
||
<ol class="arabic simple">
|
||
<li><p>Downloads from official source</p></li>
|
||
<li><p>Verifies integrity</p></li>
|
||
<li><p>Caches locally</p></li>
|
||
<li><p>Preprocesses for your framework</p></li>
|
||
</ol>
|
||
<p><strong>Purpose</strong>: Prove your CNN implementation works on real natural images (75%+ accuracy target)</p>
|
||
<p><strong>Milestone goal</strong>: Build LeNet-style CNN achieving 75%+ accuracy—demonstrating spatial intelligence.</p>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="dataset-selection-rationale">
|
||
<h2>Dataset Selection Rationale<a class="headerlink" href="#dataset-selection-rationale" title="Permalink to this heading">#</a></h2>
|
||
<section id="why-these-specific-datasets">
|
||
<h3>Why These Specific Datasets?<a class="headerlink" href="#why-these-specific-datasets" title="Permalink to this heading">#</a></h3>
|
||
<p><strong>TinyDigits (not full MNIST):</strong></p>
|
||
<ul class="simple">
|
||
<li><p>100× faster training iterations</p></li>
|
||
<li><p>Ships with repo (no download)</p></li>
|
||
<li><p>Same conceptual challenges</p></li>
|
||
<li><p>Perfect for learning and debugging</p></li>
|
||
</ul>
|
||
<p><strong>TinyTalks (custom dataset):</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Designed for educational progression</p></li>
|
||
<li><p>Scaffolded difficulty levels</p></li>
|
||
<li><p>Character-level tokenization friendly</p></li>
|
||
<li><p>Engaging conversational format</p></li>
|
||
</ul>
|
||
<p><strong>MNIST (when scaling up):</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Industry standard benchmark</p></li>
|
||
<li><p>Validates your implementation</p></li>
|
||
<li><p>Comparable to published results</p></li>
|
||
<li><p>95%+ accuracy is achievable milestone</p></li>
|
||
</ul>
|
||
<p><strong>CIFAR-10 (for CNN validation):</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Natural images (harder than digits)</p></li>
|
||
<li><p>RGB channels (multi-dimensional)</p></li>
|
||
<li><p>Standard CNN benchmark</p></li>
|
||
<li><p>75%+ with basic CNN proves it works</p></li>
|
||
</ul>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="accessing-datasets">
|
||
<h2>Accessing Datasets<a class="headerlink" href="#accessing-datasets" title="Permalink to this heading">#</a></h2>
|
||
<section id="for-students">
|
||
<h3>For Students<a class="headerlink" href="#for-students" title="Permalink to this heading">#</a></h3>
|
||
<p><strong>You don’t need to manually download anything!</strong></p>
|
||
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Just run milestone scripts</span>
|
||
<span class="nb">cd</span><span class="w"> </span>milestones/03_1986_mlp
|
||
python<span class="w"> </span>01_rumelhart_tinydigits.py<span class="w"> </span><span class="c1"># Uses shipped TinyDigits</span>
|
||
|
||
python<span class="w"> </span>02_rumelhart_mnist.py<span class="w"> </span><span class="c1"># Auto-downloads MNIST if needed</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>The milestones handle all data loading automatically.</p>
|
||
</section>
|
||
<section id="for-developers-researchers">
|
||
<h3>For Developers/Researchers<a class="headerlink" href="#for-developers-researchers" title="Permalink to this heading">#</a></h3>
|
||
<p><strong>Direct dataset access:</strong></p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Shipped datasets (always available)</span>
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">datasets.tinydigits</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_tinydigits</span>
|
||
<span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">load_tinydigits</span><span class="p">()</span>
|
||
|
||
<span class="kn">from</span><span class="w"> </span><span class="nn">datasets.tinytalks</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_tinytalks</span>
|
||
<span class="n">conversations</span> <span class="o">=</span> <span class="n">load_tinytalks</span><span class="p">()</span>
|
||
|
||
<span class="c1"># Downloaded datasets (through milestones)</span>
|
||
<span class="c1"># See milestones/data_manager.py for download utilities</span>
|
||
</pre></div>
|
||
</div>
|
||
</section>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="dataset-sizes-summary">
|
||
<h2>Dataset Sizes Summary<a class="headerlink" href="#dataset-sizes-summary" title="Permalink to this heading">#</a></h2>
|
||
<div class="pst-scrollable-table-container"><table class="table">
|
||
<thead>
|
||
<tr class="row-odd"><th class="head"><p>Dataset</p></th>
|
||
<th class="head"><p>Size</p></th>
|
||
<th class="head"><p>Samples</p></th>
|
||
<th class="head"><p>Ships With Repo</p></th>
|
||
<th class="head"><p>Purpose</p></th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="row-even"><td><p>TinyDigits</p></td>
|
||
<td><p>310 KB</p></td>
|
||
<td><p>1,200</p></td>
|
||
<td><p>Yes</p></td>
|
||
<td><p>Fast MLP/CNN iteration</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td><p>TinyTalks</p></td>
|
||
<td><p>40 KB</p></td>
|
||
<td><p>350 pairs</p></td>
|
||
<td><p>Yes</p></td>
|
||
<td><p>Transformer learning</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td><p>MNIST</p></td>
|
||
<td><p>10 MB</p></td>
|
||
<td><p>70,000</p></td>
|
||
<td><p>Downloads</p></td>
|
||
<td><p>MLP validation</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td><p>CIFAR-10</p></td>
|
||
<td><p>170 MB</p></td>
|
||
<td><p>60,000</p></td>
|
||
<td><p>Downloads</p></td>
|
||
<td><p>CNN validation</p></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
<p><strong>Total shipped</strong>: ~350 KB<br />
|
||
<strong>Total with benchmarks</strong>: ~180 MB</p>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="why-ship-with-repo-matters">
|
||
<h2>Why Ship-with-Repo Matters<a class="headerlink" href="#why-ship-with-repo-matters" title="Permalink to this heading">#</a></h2>
|
||
<div style="background: #e3f2fd; padding: 1.5rem; border-radius: 0.5rem; margin: 1.5rem 0;">
|
||
<p><strong>Traditional ML courses:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>“Download MNIST (10 MB)”</p></li>
|
||
<li><p>“Download CIFAR-10 (170 MB)”</p></li>
|
||
<li><p>Wait for downloads before starting</p></li>
|
||
<li><p>Large files in Git (bad practice)</p></li>
|
||
</ul>
|
||
<p><strong>TinyTorch approach:</strong></p>
|
||
<ul class="simple">
|
||
<li><p>Clone repo → Immediately start learning</p></li>
|
||
<li><p>Train first model in under 1 minute</p></li>
|
||
<li><p>Full benchmarks download only when scaling</p></li>
|
||
<li><p>Git repo stays small and fast</p></li>
|
||
</ul>
|
||
<p><strong>Educational benefit</strong>: Students see working models within minutes, not hours.</p>
|
||
</div>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="frequently-asked-questions">
|
||
<h2>Frequently Asked Questions<a class="headerlink" href="#frequently-asked-questions" title="Permalink to this heading">#</a></h2>
|
||
<p><strong>Q: Why not use full MNIST from the start?</strong><br />
|
||
A: TinyDigits trains 100× faster, enabling rapid iteration during learning. MNIST validates your complete implementation later.</p>
|
||
<p><strong>Q: Can I use my own datasets?</strong><br />
|
||
A: Absolutely! TinyTorch is a real framework—add your data loading code just like PyTorch.</p>
|
||
<p><strong>Q: Why ship datasets in Git?</strong><br />
|
||
A: 350 KB is negligible (smaller than many images), and it enables offline learning with instant iteration.</p>
|
||
<p><strong>Q: Where does CIFAR-10 download from?</strong><br />
|
||
A: Official sources via <code class="docutils literal notranslate"><span class="pre">milestones/data_manager.py</span></code>, with integrity verification.</p>
|
||
<p><strong>Q: Can I skip the large downloads?</strong><br />
|
||
A: Yes! You can work through most milestones using only shipped datasets. Downloaded datasets are for validation milestones.</p>
|
||
</section>
|
||
<hr class="docutils" />
|
||
<section id="related-documentation">
|
||
<h2>Related Documentation<a class="headerlink" href="#related-documentation" title="Permalink to this heading">#</a></h2>
|
||
<ul class="simple">
|
||
<li><p><a class="reference internal" href="chapters/milestones.html"><span class="doc std std-doc">Milestones Guide</span></a> - See how each dataset is used in historical achievements</p></li>
|
||
<li><p><a class="reference internal" href="student-workflow.html"><span class="doc std std-doc">Student Workflow</span></a> - Learn the development cycle</p></li>
|
||
<li><p><a class="reference internal" href="quickstart-guide.html"><span class="doc std std-doc">Quick Start</span></a> - Start building in 15 minutes</p></li>
|
||
</ul>
|
||
<p><strong>Dataset implementation details</strong>: See <code class="docutils literal notranslate"><span class="pre">datasets/tinydigits/README.md</span></code> and <code class="docutils literal notranslate"><span class="pre">datasets/tinytalks/README.md</span></code> for technical specifications.</p>
|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#design-philosophy">Design Philosophy</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#shipped-datasets-included-with-tinytorch">Shipped Datasets (Included with TinyTorch)</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="#tinydigits-handwritten-digit-recognition">TinyDigits - Handwritten Digit Recognition</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#tinytalks-conversational-q-a-dataset">TinyTalks - Conversational Q&A Dataset</a></li>
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|
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|
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#downloaded-datasets-auto-downloaded-on-demand">Downloaded Datasets (Auto-Downloaded On-Demand)</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="#mnist-handwritten-digit-classification">MNIST - Handwritten Digit Classification</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#related-documentation">Related Documentation</a></li>
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