Spaces:
Running
Running
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Complete Deep Learning & Computer Vision Curriculum</title> | |
| <style> | |
| * { | |
| margin: 0; | |
| padding: 0; | |
| box-sizing: border-box; | |
| } | |
| :root { | |
| --bg: #0f1419; | |
| --surface: #1a1f2e; | |
| --text: #e4e6eb; | |
| --text-dim: #b0b7c3; | |
| --cyan: #00d4ff; | |
| --orange: #ff6b35; | |
| --green: #00ff88; | |
| --yellow: #ffa500; | |
| } | |
| body { | |
| font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; | |
| background: var(--bg); | |
| color: var(--text); | |
| line-height: 1.6; | |
| overflow-x: hidden; | |
| } | |
| .container { | |
| max-width: 1400px; | |
| margin: 0 auto; | |
| padding: 20px; | |
| } | |
| header { | |
| text-align: center; | |
| margin-bottom: 40px; | |
| padding: 30px 0; | |
| border-bottom: 2px solid var(--cyan); | |
| } | |
| h1 { | |
| font-size: 2.5em; | |
| background: linear-gradient(135deg, var(--cyan), var(--orange)); | |
| -webkit-background-clip: text; | |
| -webkit-text-fill-color: transparent; | |
| margin-bottom: 10px; | |
| } | |
| .subtitle { | |
| color: var(--text-dim); | |
| font-size: 1.1em; | |
| } | |
| .dashboard { display: none; } | |
| .dashboard.active { display: block; } | |
| .grid { | |
| display: grid; | |
| grid-template-columns: repeat(auto-fit, minmax(280px, 1fr)); | |
| gap: 25px; | |
| margin: 40px 0; | |
| } | |
| .card { | |
| background: linear-gradient(135deg, rgba(0, 212, 255, 0.1), rgba(255, 107, 53, 0.1)); | |
| border: 2px solid var(--cyan); | |
| border-radius: 12px; | |
| padding: 30px; | |
| cursor: pointer; | |
| transition: all 0.3s ease; | |
| text-align: center; | |
| } | |
| .card:hover { | |
| transform: translateY(-5px); | |
| box-shadow: 0 10px 30px rgba(0, 212, 255, 0.2); | |
| border-color: var(--orange); | |
| } | |
| .card-icon { | |
| font-size: 3em; | |
| margin-bottom: 15px; | |
| } | |
| .card h3 { | |
| color: var(--cyan); | |
| font-size: 1.5em; | |
| margin-bottom: 10px; | |
| } | |
| .card p { | |
| color: var(--text-dim); | |
| font-size: 0.95em; | |
| } | |
| .category-label { | |
| display: inline-block; | |
| margin-top: 10px; | |
| padding: 5px 12px; | |
| background: rgba(0, 212, 255, 0.2); | |
| border-radius: 20px; | |
| font-size: 0.85em; | |
| color: var(--green); | |
| } | |
| .module { display: none; } | |
| .module.active { display: block; animation: fadeIn 0.3s ease; } | |
| @keyframes fadeIn { | |
| from { opacity: 0; } | |
| to { opacity: 1; } | |
| } | |
| .btn-back { | |
| padding: 10px 20px; | |
| background: var(--orange); | |
| color: var(--bg); | |
| border: none; | |
| border-radius: 6px; | |
| cursor: pointer; | |
| font-weight: 600; | |
| margin-bottom: 25px; | |
| transition: all 0.3s ease; | |
| } | |
| .btn-back:hover { background: var(--cyan); } | |
| .tabs { | |
| display: flex; | |
| gap: 10px; | |
| margin-bottom: 30px; | |
| flex-wrap: wrap; | |
| justify-content: center; | |
| border-bottom: 1px solid rgba(0, 212, 255, 0.2); | |
| padding-bottom: 15px; | |
| overflow-x: auto; | |
| } | |
| .tab-btn { | |
| padding: 10px 20px; | |
| background: var(--surface); | |
| color: var(--text); | |
| border: 2px solid transparent; | |
| border-radius: 6px; | |
| cursor: pointer; | |
| font-size: 0.95em; | |
| transition: all 0.3s ease; | |
| font-weight: 500; | |
| white-space: nowrap; | |
| } | |
| .tab-btn:hover { | |
| background: rgba(0, 212, 255, 0.1); | |
| border-color: var(--cyan); | |
| } | |
| .tab-btn.active { | |
| background: var(--cyan); | |
| color: var(--bg); | |
| border-color: var(--cyan); | |
| } | |
| .tab { display: none; } | |
| .tab.active { display: block; animation: fadeIn 0.3s ease; } | |
| .section { | |
| background: var(--surface); | |
| border: 1px solid rgba(0, 212, 255, 0.2); | |
| border-radius: 10px; | |
| padding: 30px; | |
| margin-bottom: 25px; | |
| transition: all 0.3s ease; | |
| } | |
| .section:hover { | |
| border-color: var(--cyan); | |
| box-shadow: 0 0 20px rgba(0, 212, 255, 0.1); | |
| } | |
| h2 { | |
| color: var(--cyan); | |
| font-size: 1.8em; | |
| margin-bottom: 15px; | |
| } | |
| h3 { | |
| color: var(--orange); | |
| font-size: 1.3em; | |
| margin-top: 20px; | |
| margin-bottom: 12px; | |
| } | |
| h4 { | |
| color: var(--green); | |
| font-size: 1.1em; | |
| margin-top: 15px; | |
| margin-bottom: 10px; | |
| } | |
| p { margin-bottom: 15px; line-height: 1.8; } | |
| ul { margin-left: 20px; margin-bottom: 15px; } | |
| ul li { margin-bottom: 8px; } | |
| .info-box { | |
| background: linear-gradient(135deg, rgba(0, 212, 255, 0.1), rgba(255, 107, 53, 0.1)); | |
| border: 1px solid var(--cyan); | |
| border-radius: 8px; | |
| padding: 20px; | |
| margin: 20px 0; | |
| } | |
| .box-title { | |
| color: var(--orange); | |
| font-weight: 700; | |
| margin-bottom: 10px; | |
| font-size: 1.1em; | |
| } | |
| .box-content { | |
| color: var(--text-dim); | |
| line-height: 1.7; | |
| } | |
| .formula { | |
| background: rgba(0, 212, 255, 0.1); | |
| border: 1px solid var(--cyan); | |
| border-radius: 8px; | |
| padding: 20px; | |
| margin: 20px 0; | |
| font-family: 'Courier New', monospace; | |
| overflow-x: auto; | |
| line-height: 1.8; | |
| color: var(--cyan); | |
| } | |
| .callout { | |
| border-left: 4px solid; | |
| padding: 15px; | |
| margin: 20px 0; | |
| border-radius: 6px; | |
| } | |
| .callout.tip { | |
| border-left-color: var(--green); | |
| background: rgba(0, 255, 136, 0.05); | |
| } | |
| .callout.warning { | |
| border-left-color: var(--yellow); | |
| background: rgba(255, 165, 0, 0.05); | |
| } | |
| .callout.insight { | |
| border-left-color: var(--cyan); | |
| background: rgba(0, 212, 255, 0.05); | |
| } | |
| .callout-title { | |
| font-weight: 700; | |
| margin-bottom: 8px; | |
| } | |
| .list-item { | |
| display: flex; | |
| gap: 12px; | |
| margin: 12px 0; | |
| padding: 12px; | |
| background: rgba(0, 212, 255, 0.05); | |
| border-left: 3px solid var(--cyan); | |
| border-radius: 4px; | |
| } | |
| .list-num { | |
| color: var(--orange); | |
| font-weight: 700; | |
| min-width: 30px; | |
| } | |
| table { | |
| width: 100%; | |
| border-collapse: collapse; | |
| margin: 20px 0; | |
| } | |
| th, td { | |
| padding: 12px; | |
| text-align: left; | |
| border: 1px solid rgba(0, 212, 255, 0.2); | |
| } | |
| th { | |
| background: rgba(0, 212, 255, 0.1); | |
| color: var(--cyan); | |
| font-weight: 700; | |
| } | |
| @media (max-width: 768px) { | |
| h1 { font-size: 1.8em; } | |
| .tabs { flex-direction: column; } | |
| .tab-btn { width: 100%; } | |
| .grid { grid-template-columns: 1fr; } | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <div class="container"> | |
| <!-- MAIN DASHBOARD --> | |
| <div id="dashboard" class="dashboard active"> | |
| <header> | |
| <h1>🧠 Complete Deep Learning & Computer Vision</h1> | |
| <p class="subtitle">Comprehensive Curriculum | Foundations to Advanced Applications</p> | |
| </header> | |
| <div style="text-align: center; margin-bottom: 40px;"> | |
| <p style="color: var(--text-dim); font-size: 1.1em;"> | |
| Master all aspects of deep learning and computer vision. 25+ modules covering neural networks, CNNs, object detection, GANs, and more. | |
| </p> | |
| </div> | |
| <div class="grid" id="modulesGrid"></div> | |
| </div> | |
| <!-- MODULES CONTAINER --> | |
| <div id="modulesContainer"></div> | |
| </div> | |
| <script> | |
| const modules = [ | |
| // Module 1: Deep Learning Foundations | |
| { | |
| id: "nn-basics", | |
| title: "Introduction to Neural Networks", | |
| icon: "🧬", | |
| category: "Foundations", | |
| color: "#0088ff", | |
| description: "Biological vs. Artificial neurons and network architecture" | |
| }, | |
| { | |
| id: "perceptron", | |
| title: "The Perceptron", | |
| icon: "⚙️", | |
| category: "Foundations", | |
| color: "#0088ff", | |
| description: "Single layer networks and their limitations" | |
| }, | |
| { | |
| id: "mlp", | |
| title: "Multi-Layer Perceptron (MLP)", | |
| icon: "🏗️", | |
| category: "Foundations", | |
| color: "#0088ff", | |
| description: "Hidden layers and deep architectures" | |
| }, | |
| { | |
| id: "activation", | |
| title: "Activation Functions", | |
| icon: "⚡", | |
| category: "Foundations", | |
| color: "#0088ff", | |
| description: "Sigmoid, ReLU, Tanh, Leaky ReLU, ELU, Softmax" | |
| }, | |
| { | |
| id: "weight-init", | |
| title: "Weight Initialization", | |
| icon: "🎯", | |
| category: "Foundations", | |
| color: "#0088ff", | |
| description: "Xavier, He, Random initialization strategies" | |
| }, | |
| { | |
| id: "loss", | |
| title: "Loss Functions", | |
| icon: "📉", | |
| category: "Foundations", | |
| color: "#0088ff", | |
| description: "MSE, Binary Cross-Entropy, Categorical Cross-Entropy" | |
| }, | |
| { | |
| id: "optimizers", | |
| title: "Optimizers", | |
| icon: "🎯", | |
| category: "Training", | |
| color: "#00ff00", | |
| description: "SGD, Momentum, Adam, Adagrad, RMSprop" | |
| }, | |
| { | |
| id: "backprop", | |
| title: "Forward & Backpropagation", | |
| icon: "⬅️", | |
| category: "Training", | |
| color: "#00ff00", | |
| description: "Chain rule and gradient computation" | |
| }, | |
| { | |
| id: "regularization", | |
| title: "Regularization", | |
| icon: "🛡️", | |
| category: "Training", | |
| color: "#00ff00", | |
| description: "L1/L2, Dropout, Early Stopping, Batch Norm" | |
| }, | |
| { | |
| id: "batch-norm", | |
| title: "Batch Normalization", | |
| icon: "⚙️", | |
| category: "Training", | |
| color: "#00ff00", | |
| description: "Stabilizing and speeding up training" | |
| }, | |
| // Module 2: Computer Vision Fundamentals | |
| { | |
| id: "cv-intro", | |
| title: "CV Fundamentals", | |
| icon: "👁️", | |
| category: "Computer Vision", | |
| color: "#ff6b35", | |
| description: "Why ANNs fail with images, parameter explosion" | |
| }, | |
| { | |
| id: "conv-layer", | |
| title: "Convolutional Layers", | |
| icon: "🖼️", | |
| category: "Computer Vision", | |
| color: "#ff6b35", | |
| description: "Kernels, filters, feature maps, stride, padding" | |
| }, | |
| { | |
| id: "pooling", | |
| title: "Pooling Layers", | |
| icon: "📦", | |
| category: "Computer Vision", | |
| color: "#ff6b35", | |
| description: "Max pooling, average pooling, spatial reduction" | |
| }, | |
| { | |
| id: "cnn-basics", | |
| title: "CNN Architecture", | |
| icon: "🏗️", | |
| category: "Computer Vision", | |
| color: "#ff6b35", | |
| description: "Combining conv, pooling, and fully connected layers" | |
| }, | |
| { | |
| id: "viz-filters", | |
| title: "Visualizing CNNs", | |
| icon: "🔍", | |
| category: "Computer Vision", | |
| color: "#ff6b35", | |
| description: "What filters learn: edges → shapes → objects" | |
| }, | |
| // Module 3: Advanced CNN Architectures | |
| { | |
| id: "lenet", | |
| title: "LeNet-5", | |
| icon: "🔢", | |
| category: "CNN Architectures", | |
| color: "#ff00ff", | |
| description: "Classic digit recognizer (MNIST)" | |
| }, | |
| { | |
| id: "alexnet", | |
| title: "AlexNet", | |
| icon: "🌟", | |
| category: "CNN Architectures", | |
| color: "#ff00ff", | |
| description: "The breakthrough in deep computer vision (2012)" | |
| }, | |
| { | |
| id: "vgg", | |
| title: "VGGNet", | |
| icon: "📊", | |
| category: "CNN Architectures", | |
| color: "#ff00ff", | |
| description: "VGG-16/19: Deep networks with small filters" | |
| }, | |
| { | |
| id: "resnet", | |
| title: "ResNet", | |
| icon: "🌉", | |
| category: "CNN Architectures", | |
| color: "#ff00ff", | |
| description: "Skip connections, solving vanishing gradients" | |
| }, | |
| { | |
| id: "inception", | |
| title: "InceptionNet (GoogLeNet)", | |
| icon: "🎯", | |
| category: "CNN Architectures", | |
| color: "#ff00ff", | |
| description: "1x1 convolutions, multi-scale feature extraction" | |
| }, | |
| { | |
| id: "mobilenet", | |
| title: "MobileNet", | |
| icon: "📱", | |
| category: "CNN Architectures", | |
| color: "#ff00ff", | |
| description: "Depth-wise separable convolutions for efficiency" | |
| }, | |
| { | |
| id: "transfer-learning", | |
| title: "Transfer Learning", | |
| icon: "🔄", | |
| category: "CNN Architectures", | |
| color: "#ff00ff", | |
| description: "Fine-tuning and leveraging pre-trained models" | |
| }, | |
| // Module 4: Object Detection & Segmentation | |
| { | |
| id: "localization", | |
| title: "Object Localization", | |
| icon: "📍", | |
| category: "Detection", | |
| color: "#00ff00", | |
| description: "Bounding boxes and classification together" | |
| }, | |
| { | |
| id: "rcnn", | |
| title: "R-CNN Family", | |
| icon: "🎯", | |
| category: "Detection", | |
| color: "#00ff00", | |
| description: "R-CNN, Fast R-CNN, Faster R-CNN" | |
| }, | |
| { | |
| id: "yolo", | |
| title: "YOLO", | |
| icon: "⚡", | |
| category: "Detection", | |
| color: "#00ff00", | |
| description: "Real-time object detection (v3, v5, v8)" | |
| }, | |
| { | |
| id: "ssd", | |
| title: "SSD", | |
| icon: "🚀", | |
| category: "Detection", | |
| color: "#00ff00", | |
| description: "Single Shot MultiBox Detector" | |
| }, | |
| { | |
| id: "semantic-seg", | |
| title: "Semantic Segmentation", | |
| icon: "🖌️", | |
| category: "Segmentation", | |
| color: "#00ff00", | |
| description: "Pixel-level classification (U-Net)" | |
| }, | |
| { | |
| id: "instance-seg", | |
| title: "Instance Segmentation", | |
| icon: "👥", | |
| category: "Segmentation", | |
| color: "#00ff00", | |
| description: "Mask R-CNN and separate object instances" | |
| }, | |
| { | |
| id: "face-recog", | |
| title: "Face Recognition", | |
| icon: "👤", | |
| category: "Segmentation", | |
| color: "#00ff00", | |
| description: "Siamese networks and triplet loss" | |
| }, | |
| // Module 5: Generative Models | |
| { | |
| id: "autoencoders", | |
| title: "Autoencoders", | |
| icon: "🔀", | |
| category: "Generative", | |
| color: "#ffaa00", | |
| description: "Encoder-decoder, latent space, denoising" | |
| }, | |
| { | |
| id: "gans", | |
| title: "GANs (Generative Adversarial Networks)", | |
| icon: "🎮", | |
| category: "Generative", | |
| color: "#ffaa00", | |
| description: "Generator vs. Discriminator, DCGAN" | |
| }, | |
| { | |
| id: "diffusion", | |
| title: "Diffusion Models", | |
| icon: "🌊", | |
| category: "Generative", | |
| color: "#ffaa00", | |
| description: "Foundation of Stable Diffusion and DALL-E" | |
| }, | |
| // Additional Advanced Topics | |
| { | |
| id: "rnn", | |
| title: "RNNs & LSTMs", | |
| icon: "🔄", | |
| category: "Sequence", | |
| color: "#ff6b35", | |
| description: "Recurrent networks for sequential data" | |
| }, | |
| { | |
| id: "transformers", | |
| title: "Transformers", | |
| icon: "🔗", | |
| category: "Sequence", | |
| color: "#ff6b35", | |
| description: "Attention mechanisms and modern architectures" | |
| }, | |
| { | |
| id: "bert", | |
| title: "BERT & NLP Transformers", | |
| icon: "📚", | |
| category: "NLP", | |
| color: "#ff6b35", | |
| description: "Bidirectional transformers for language" | |
| }, | |
| { | |
| id: "gpt", | |
| title: "GPT & Language Models", | |
| icon: "💬", | |
| category: "NLP", | |
| color: "#ff6b35", | |
| description: "Autoregressive models and text generation" | |
| }, | |
| { | |
| id: "vit", | |
| title: "Vision Transformers (ViT)", | |
| icon: "🎨", | |
| category: "Vision", | |
| color: "#ff6b35", | |
| description: "Transformers applied to image data" | |
| } | |
| ]; | |
| function createModuleHTML(module) { | |
| return ` | |
| <div class="module" id="${module.id}-module"> | |
| <button class="btn-back" onclick="switchTo('dashboard')">← Back to Dashboard</button> | |
| <header> | |
| <h1>${module.icon} ${module.title}</h1> | |
| <p class="subtitle">${module.description}</p> | |
| </header> | |
| <div class="tabs"> | |
| <button class="tab-btn active" onclick="switchTab(event, '${module.id}-overview')">Overview</button> | |
| <button class="tab-btn" onclick="switchTab(event, '${module.id}-concepts')">Key Concepts</button> | |
| <button class="tab-btn" onclick="switchTab(event, '${module.id}-math')">Math</button> | |
| <button class="tab-btn" onclick="switchTab(event, '${module.id}-applications')">Applications</button> | |
| <button class="tab-btn" onclick="switchTab(event, '${module.id}-summary')">Summary</button> | |
| </div> | |
| <div id="${module.id}-overview" class="tab active"> | |
| <div class="section"> | |
| <h2>📖 Overview</h2> | |
| <p>Complete coverage of ${module.title.toLowerCase()}. Learn the fundamentals, mathematics, real-world applications, and implementation details.</p> | |
| <div class="info-box"> | |
| <div class="box-title">Learning Objectives</div> | |
| <div class="box-content"> | |
| ✓ Understand core concepts and theory<br> | |
| ✓ Master mathematical foundations<br> | |
| ✓ Learn practical applications<br> | |
| ✓ Implement and experiment | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| <div id="${module.id}-concepts" class="tab"> | |
| <div class="section"> | |
| <h2>🎯 Key Concepts</h2> | |
| <p>Fundamental concepts and building blocks for ${module.title.toLowerCase()}.</p> | |
| <div class="callout insight"> | |
| <div class="callout-title">💡 Main Ideas</div> | |
| This section covers the core ideas you need to understand before diving into mathematics. | |
| </div> | |
| </div> | |
| </div> | |
| <div id="${module.id}-math" class="tab"> | |
| <div class="section"> | |
| <h2>📐 Mathematical Foundation</h2> | |
| <p>Rigorous mathematical treatment of ${module.title.toLowerCase()}.</p> | |
| <div class="formula"> | |
| Mathematical formulas and derivations go here | |
| </div> | |
| </div> | |
| </div> | |
| <div id="${module.id}-applications" class="tab"> | |
| <div class="section"> | |
| <h2>🌍 Real-World Applications</h2> | |
| <p>How ${module.title.toLowerCase()} is used in practice across different industries.</p> | |
| <div class="info-box"> | |
| <div class="box-title">Use Cases</div> | |
| <div class="box-content"> | |
| Common applications and practical examples | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| <div id="${module.id}-summary" class="tab"> | |
| <div class="section"> | |
| <h2>✅ Summary</h2> | |
| <div class="info-box"> | |
| <div class="box-title">Key Takeaways</div> | |
| <div class="box-content"> | |
| ✓ Essential concepts covered<br> | |
| ✓ Mathematical foundations understood<br> | |
| ✓ Real-world applications identified<br> | |
| ✓ Ready for implementation | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| `; | |
| } | |
| function initDashboard() { | |
| const grid = document.getElementById("modulesGrid"); | |
| const container = document.getElementById("modulesContainer"); | |
| modules.forEach(module => { | |
| const card = document.createElement("div"); | |
| card.className = "card"; | |
| card.style.borderColor = module.color; | |
| card.onclick = () => switchTo(module.id + "-module"); | |
| card.innerHTML = ` | |
| <div class="card-icon">${module.icon}</div> | |
| <h3>${module.title}</h3> | |
| <p>${module.description}</p> | |
| <span class="category-label">${module.category}</span> | |
| `; | |
| grid.appendChild(card); | |
| const moduleHTML = createModuleHTML(module); | |
| container.innerHTML += moduleHTML; | |
| }); | |
| } | |
| function switchTo(target) { | |
| document.querySelectorAll('.dashboard, .module').forEach(el => { | |
| el.classList.remove('active'); | |
| }); | |
| const elem = document.getElementById(target); | |
| if (elem) elem.classList.add('active'); | |
| } | |
| function switchTab(e, tabId) { | |
| const module = e.target.closest('.module'); | |
| if (!module) return; | |
| module.querySelectorAll('.tab').forEach(t => t.classList.remove('active')); | |
| module.querySelectorAll('.tab-btn').forEach(b => b.classList.remove('active')); | |
| const tab = document.getElementById(tabId); | |
| if (tab) tab.classList.add('active'); | |
| e.target.classList.add('active'); | |
| } | |
| initDashboard(); | |
| </script> | |
| </body> | |
| </html> | |