mirror of
https://github.com/harvard-edge/cs249r_book.git
synced 2026-05-02 02:29:16 -05:00
871d1f473a
- Update remaining 1957→1958 references across all documentation - Add tito dev commands (preflight, export, validate) to CLI reference - Update CLI validation script to recognize new dev subcommands - Fix milestone year references in tests and workflow code - Update timeline visualization JavaScript This completes the Perceptron year standardization to align with the publication year and academic citation format (rosenblatt1958perceptron). Cherry-picked from: ebf3fb17b (feature/tito-dev-validate)
246 lines
10 KiB
JavaScript
246 lines
10 KiB
JavaScript
/**
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* Interactive ML History Timeline
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* Handles popup functionality for milestone cards
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*/
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document.addEventListener('DOMContentLoaded', function() {
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const timelineData = {
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perceptron: {
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year: "1958",
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title: "The Perceptron",
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researcher: "Frank Rosenblatt",
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subtitle: "The first trainable neural network proves machines can learn from data",
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achievement: "Binary classification with gradient descent",
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architecture: "Input → Linear → Sigmoid → Output",
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whatYouBuild: [
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"Binary classification with gradient descent",
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"Simple but revolutionary architecture",
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"YOUR Linear layer recreates history"
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],
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systemsInsights: [
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"Memory: O(n) parameters",
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"Compute: O(n) operations",
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"Limitation: Only linearly separable problems"
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],
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modules: "After Modules 02-04",
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expectedResults: "~50% (untrained) → 95%+ (trained) accuracy",
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commands: [
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"tito milestone run perceptron"
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]
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},
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xor: {
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year: "1969",
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title: "The XOR Crisis",
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researcher: "Minsky & Papert",
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subtitle: "Hidden layers solve non-linear problems that nearly ended AI research",
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achievement: "Non-linear learning through hidden representations",
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architecture: "Input → Linear → ReLU → Linear → Output",
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whatYouBuild: [
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"Hidden layers enable non-linear solutions",
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"Multi-layer networks break through limitations",
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"YOUR autograd makes it possible"
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],
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systemsInsights: [
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"Memory: O(n²) with hidden layers",
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"Compute: O(n²) operations",
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"Breakthrough: Hidden representations"
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],
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modules: "After Modules 02-06",
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expectedResults: "50% (single layer) → 100% (multi-layer) on XOR",
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commands: [
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"tito milestone run xor"
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]
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},
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mlp: {
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year: "1986",
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title: "MLP Revival",
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researcher: "Backpropagation Era",
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subtitle: "Backpropagation enables training deep networks on real datasets",
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achievement: "Multi-class digit recognition",
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architecture: "Images → Flatten → Linear → ReLU → Linear → ReLU → Linear → Classes",
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whatYouBuild: [
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"Multi-class digit recognition",
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"Complete training pipelines",
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"YOUR optimizers achieve 95%+ accuracy"
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],
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systemsInsights: [
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"Memory: ~100K parameters for MNIST",
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"Compute: Dense matrix operations",
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"Architecture: Multi-layer feature learning"
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],
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modules: "After Modules 02-08",
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expectedResults: "95%+ accuracy on MNIST",
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commands: [
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"tito milestone run mlp"
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]
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},
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cnn: {
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year: "1998",
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title: "CNN Revolution",
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researcher: "Yann LeCun",
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subtitle: "CNNs exploit spatial structure for computer vision—enabling modern AI",
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achievement: "Spatial intelligence for computer vision",
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architecture: "Images → Conv → ReLU → Pool → Conv → ReLU → Pool → Flatten → Linear → Classes",
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whatYouBuild: [
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"Convolutional feature extraction",
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"Natural image classification (CIFAR-10)",
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"YOUR Conv2d + MaxPool2d unlock spatial intelligence"
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],
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systemsInsights: [
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"Memory: ~1M parameters (weight sharing reduces vs dense)",
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"Compute: Convolution is intensive but parallelizable",
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"Architecture: Local connectivity + translation invariance"
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],
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modules: "After Modules 02-09",
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expectedResults: "75%+ accuracy on CIFAR-10 ✨",
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commands: [
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"tito milestone run cnn"
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],
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northStar: true
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},
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transformer: {
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year: "2017",
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title: "Transformer Era",
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researcher: "Vaswani et al.",
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subtitle: "Attention mechanism launches the LLM revolution (GPT, BERT, ChatGPT)",
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achievement: "Self-attention for language understanding",
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architecture: "Tokens → Embeddings → Attention → FFN → ... → Attention → Output",
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whatYouBuild: [
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"Self-attention mechanisms",
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"Autoregressive text generation",
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"YOUR attention implementation generates language"
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],
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systemsInsights: [
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"Memory: O(n²) attention requires careful management",
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"Compute: Highly parallelizable",
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"Architecture: Long-range dependencies"
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],
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modules: "After Modules 02-13",
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expectedResults: "Loss < 1.5, coherent responses to questions",
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commands: [
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"tito milestone run transformer"
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]
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},
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olympics: {
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year: "2018",
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title: "MLPerf Torch Olympics",
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researcher: "MLCommons (founded 2018)",
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subtitle: "Systematic optimization becomes essential as models grow larger",
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achievement: "Production-ready optimization",
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architecture: "Profile → Compress → Accelerate",
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whatYouBuild: [
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"Performance profiling and bottleneck analysis",
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"Model compression (quantization + pruning)",
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"Inference acceleration (KV-cache + batching)"
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],
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systemsInsights: [
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"Memory: 4-16× compression through quantization/pruning",
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"Speed: 12-40× faster generation with KV-cache + batching",
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"Workflow: Systematic 'measure → optimize → validate' methodology"
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],
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modules: "After Modules 14-18",
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expectedResults: "8-16× smaller models, 12-40× faster inference",
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commands: [
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"tito milestone run mlperf"
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]
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}
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};
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// Create popup HTML if not exists
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let popup = document.getElementById('ml-timeline-popup');
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if (!popup) {
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popup = document.createElement('div');
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popup.id = 'ml-timeline-popup';
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popup.className = 'ml-timeline-popup';
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popup.innerHTML = '<div class="ml-timeline-popup-content"></div>';
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document.body.appendChild(popup);
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}
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// Handle clicks on timeline items
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document.querySelectorAll('.ml-timeline-content').forEach(card => {
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card.addEventListener('click', function(e) {
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const item = this.closest('.ml-timeline-item');
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const milestoneType = item.classList[1]; // Get the milestone class (perceptron, xor, etc.)
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const data = timelineData[milestoneType];
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if (!data) return;
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const popupContent = popup.querySelector('.ml-timeline-popup-content');
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popupContent.innerHTML = `
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<button class="ml-timeline-popup-close" aria-label="Close">×</button>
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<h3>
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<span class="ml-timeline-popup-year">${data.year}</span>
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${data.title}
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</h3>
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<p class="ml-timeline-popup-subtitle">${data.subtitle}</p>
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${data.northStar ? '<div style="background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%); padding: 1rem; border-radius: 0.5rem; margin: 1rem 0; border-left: 4px solid #f59e0b;"><strong>🎯 North Star Achievement</strong> — This is a major milestone in your TinyTorch journey!</div>' : ''}
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<div class="ml-timeline-popup-section">
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<h4>The ${data.researcher} Breakthrough</h4>
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<p><strong>${data.achievement}</strong></p>
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</div>
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<div class="ml-timeline-popup-section">
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<h4>Architecture</h4>
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<div class="ml-timeline-popup-code">${data.architecture}</div>
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</div>
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<div class="ml-timeline-popup-section">
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<h4>What You'll Build</h4>
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<ul>
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${data.whatYouBuild.map(item => `<li>${item}</li>`).join('')}
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</ul>
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</div>
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<div class="ml-timeline-popup-section">
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<h4>Systems Insights</h4>
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<ul>
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${data.systemsInsights.map(item => `<li>${item}</li>`).join('')}
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</ul>
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</div>
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<div class="ml-timeline-popup-metrics">
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<div class="ml-timeline-popup-metric">
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<div class="ml-timeline-popup-metric-label">Prerequisites</div>
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<div class="ml-timeline-popup-metric-value">${data.modules}</div>
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</div>
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<div class="ml-timeline-popup-metric">
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<div class="ml-timeline-popup-metric-label">Expected Results</div>
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<div class="ml-timeline-popup-metric-value">${data.expectedResults}</div>
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</div>
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</div>
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<div class="ml-timeline-popup-section">
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<h4>Try It Yourself</h4>
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<div class="ml-timeline-popup-code">${data.commands.join('\n')}</div>
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</div>
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`;
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popup.classList.add('active');
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// Close button handler
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const closeBtn = popupContent.querySelector('.ml-timeline-popup-close');
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closeBtn.addEventListener('click', function() {
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popup.classList.remove('active');
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});
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});
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});
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// Close popup on background click
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popup.addEventListener('click', function(e) {
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if (e.target === popup) {
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popup.classList.remove('active');
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}
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});
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// Close popup on ESC key
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document.addEventListener('keydown', function(e) {
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if (e.key === 'Escape' && popup.classList.contains('active')) {
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popup.classList.remove('active');
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}
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});
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});
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