Team Ai
Apppublic

AashishAIHub/DeepLearning

sourceHugging Faceupdated 6mo agoView on Hugging Face
0likes
Deep Learning Complete Curriculum.html770 linesDownload Raw Back to root
1<!DOCTYPE html>2<html lang="en">3<head>4    <meta charset="UTF-8">5    <meta name="viewport" content="width=device-width, initial-scale=1.0">6    <title>Complete Deep Learning & Computer Vision Curriculum</title>7    <style>8        * {9            margin: 0;10            padding: 0;11            box-sizing: border-box;12        }13 14        :root {15            --bg: #0f1419;16            --surface: #1a1f2e;17            --text: #e4e6eb;18            --text-dim: #b0b7c3;19            --cyan: #00d4ff;20            --orange: #ff6b35;21            --green: #00ff88;22            --yellow: #ffa500;23        }24 25        body {26            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;27            background: var(--bg);28            color: var(--text);29            line-height: 1.6;30            overflow-x: hidden;31        }32 33        .container {34            max-width: 1400px;35            margin: 0 auto;36            padding: 20px;37        }38 39        header {40            text-align: center;41            margin-bottom: 40px;42            padding: 30px 0;43            border-bottom: 2px solid var(--cyan);44        }45 46        h1 {47            font-size: 2.5em;48            background: linear-gradient(135deg, var(--cyan), var(--orange));49            -webkit-background-clip: text;50            -webkit-text-fill-color: transparent;51            margin-bottom: 10px;52        }53 54        .subtitle {55            color: var(--text-dim);56            font-size: 1.1em;57        }58 59        .dashboard { display: none; }60        .dashboard.active { display: block; }61 62        .grid {63            display: grid;64            grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));65            gap: 25px;66            margin: 40px 0;67        }68 69        .card {70            background: linear-gradient(135deg, rgba(0, 212, 255, 0.1), rgba(255, 107, 53, 0.1));71            border: 2px solid var(--cyan);72            border-radius: 12px;73            padding: 30px;74            cursor: pointer;75            transition: all 0.3s ease;76            text-align: center;77        }78 79        .card:hover {80            transform: translateY(-5px);81            box-shadow: 0 10px 30px rgba(0, 212, 255, 0.2);82            border-color: var(--orange);83        }84 85        .card-icon {86            font-size: 3em;87            margin-bottom: 15px;88        }89 90        .card h3 {91            color: var(--cyan);92            font-size: 1.5em;93            margin-bottom: 10px;94        }95 96        .card p {97            color: var(--text-dim);98            font-size: 0.95em;99        }100 101        .category-label {102            display: inline-block;103            margin-top: 10px;104            padding: 5px 12px;105            background: rgba(0, 212, 255, 0.2);106            border-radius: 20px;107            font-size: 0.85em;108            color: var(--green);109        }110 111        .module { display: none; }112        .module.active { display: block; animation: fadeIn 0.3s ease; }113 114        @keyframes fadeIn {115            from { opacity: 0; }116            to { opacity: 1; }117        }118 119        .btn-back {120            padding: 10px 20px;121            background: var(--orange);122            color: var(--bg);123            border: none;124            border-radius: 6px;125            cursor: pointer;126            font-weight: 600;127            margin-bottom: 25px;128            transition: all 0.3s ease;129        }130 131        .btn-back:hover { background: var(--cyan); }132 133        .tabs {134            display: flex;135            gap: 10px;136            margin-bottom: 30px;137            flex-wrap: wrap;138            justify-content: center;139            border-bottom: 1px solid rgba(0, 212, 255, 0.2);140            padding-bottom: 15px;141            overflow-x: auto;142        }143 144        .tab-btn {145            padding: 10px 20px;146            background: var(--surface);147            color: var(--text);148            border: 2px solid transparent;149            border-radius: 6px;150            cursor: pointer;151            font-size: 0.95em;152            transition: all 0.3s ease;153            font-weight: 500;154            white-space: nowrap;155        }156 157        .tab-btn:hover {158            background: rgba(0, 212, 255, 0.1);159            border-color: var(--cyan);160        }161 162        .tab-btn.active {163            background: var(--cyan);164            color: var(--bg);165            border-color: var(--cyan);166        }167 168        .tab { display: none; }169        .tab.active { display: block; animation: fadeIn 0.3s ease; }170 171        .section {172            background: var(--surface);173            border: 1px solid rgba(0, 212, 255, 0.2);174            border-radius: 10px;175            padding: 30px;176            margin-bottom: 25px;177            transition: all 0.3s ease;178        }179 180        .section:hover {181            border-color: var(--cyan);182            box-shadow: 0 0 20px rgba(0, 212, 255, 0.1);183        }184 185        h2 {186            color: var(--cyan);187            font-size: 1.8em;188            margin-bottom: 15px;189        }190 191        h3 {192            color: var(--orange);193            font-size: 1.3em;194            margin-top: 20px;195            margin-bottom: 12px;196        }197 198        h4 {199            color: var(--green);200            font-size: 1.1em;201            margin-top: 15px;202            margin-bottom: 10px;203        }204 205        p { margin-bottom: 15px; line-height: 1.8; }206 207        ul { margin-left: 20px; margin-bottom: 15px; }208        ul li { margin-bottom: 8px; }209 210        .info-box {211            background: linear-gradient(135deg, rgba(0, 212, 255, 0.1), rgba(255, 107, 53, 0.1));212            border: 1px solid var(--cyan);213            border-radius: 8px;214            padding: 20px;215            margin: 20px 0;216        }217 218        .box-title {219            color: var(--orange);220            font-weight: 700;221            margin-bottom: 10px;222            font-size: 1.1em;223        }224 225        .box-content {226            color: var(--text-dim);227            line-height: 1.7;228        }229 230        .formula {231            background: rgba(0, 212, 255, 0.1);232            border: 1px solid var(--cyan);233            border-radius: 8px;234            padding: 20px;235            margin: 20px 0;236            font-family: 'Courier New', monospace;237            overflow-x: auto;238            line-height: 1.8;239            color: var(--cyan);240        }241 242        .callout {243            border-left: 4px solid;244            padding: 15px;245            margin: 20px 0;246            border-radius: 6px;247        }248 249        .callout.tip {250            border-left-color: var(--green);251            background: rgba(0, 255, 136, 0.05);252        }253 254        .callout.warning {255            border-left-color: var(--yellow);256            background: rgba(255, 165, 0, 0.05);257        }258 259        .callout.insight {260            border-left-color: var(--cyan);261            background: rgba(0, 212, 255, 0.05);262        }263 264        .callout-title {265            font-weight: 700;266            margin-bottom: 8px;267        }268 269        .list-item {270            display: flex;271            gap: 12px;272            margin: 12px 0;273            padding: 12px;274            background: rgba(0, 212, 255, 0.05);275            border-left: 3px solid var(--cyan);276            border-radius: 4px;277        }278 279        .list-num {280            color: var(--orange);281            font-weight: 700;282            min-width: 30px;283        }284 285        table {286            width: 100%;287            border-collapse: collapse;288            margin: 20px 0;289        }290 291        th, td {292            padding: 12px;293            text-align: left;294            border: 1px solid rgba(0, 212, 255, 0.2);295        }296 297        th {298            background: rgba(0, 212, 255, 0.1);299            color: var(--cyan);300            font-weight: 700;301        }302 303        @media (max-width: 768px) {304            h1 { font-size: 1.8em; }305            .tabs { flex-direction: column; }306            .tab-btn { width: 100%; }307            .grid { grid-template-columns: 1fr; }308        }309    </style>310</head>311<body>312    <div class="container">313        <!-- MAIN DASHBOARD -->314        <div id="dashboard" class="dashboard active">315            <header>316                <h1>๐Ÿง  Complete Deep Learning & Computer Vision</h1>317                <p class="subtitle">Comprehensive Curriculum | Foundations to Advanced Applications</p>318            </header>319 320            <div style="text-align: center; margin-bottom: 40px;">321                <p style="color: var(--text-dim); font-size: 1.1em;">322                    Master all aspects of deep learning and computer vision. 25+ modules covering neural networks, CNNs, object detection, GANs, and more.323                </p>324            </div>325 326            <div class="grid" id="modulesGrid"></div>327        </div>328 329        <!-- MODULES CONTAINER -->330        <div id="modulesContainer"></div>331    </div>332 333    <script>334        const modules = [335            // Module 1: Deep Learning Foundations336            {337                id: "nn-basics",338                title: "Introduction to Neural Networks",339                icon: "๐Ÿงฌ",340                category: "Foundations",341                color: "#0088ff",342                description: "Biological vs. Artificial neurons and network architecture"343            },344            {345                id: "perceptron",346                title: "The Perceptron",347                icon: "โš™๏ธ",348                category: "Foundations",349                color: "#0088ff",350                description: "Single layer networks and their limitations"351            },352            {353                id: "mlp",354                title: "Multi-Layer Perceptron (MLP)",355                icon: "๐Ÿ—๏ธ",356                category: "Foundations",357                color: "#0088ff",358                description: "Hidden layers and deep architectures"359            },360            {361                id: "activation",362                title: "Activation Functions",363                icon: "โšก",364                category: "Foundations",365                color: "#0088ff",366                description: "Sigmoid, ReLU, Tanh, Leaky ReLU, ELU, Softmax"367            },368            {369                id: "weight-init",370                title: "Weight Initialization",371                icon: "๐ŸŽฏ",372                category: "Foundations",373                color: "#0088ff",374                description: "Xavier, He, Random initialization strategies"375            },376            {377                id: "loss",378                title: "Loss Functions",379                icon: "๐Ÿ“‰",380                category: "Foundations",381                color: "#0088ff",382                description: "MSE, Binary Cross-Entropy, Categorical Cross-Entropy"383            },384            {385                id: "optimizers",386                title: "Optimizers",387                icon: "๐ŸŽฏ",388                category: "Training",389                color: "#00ff00",390                description: "SGD, Momentum, Adam, Adagrad, RMSprop"391            },392            {393                id: "backprop",394                title: "Forward & Backpropagation",395                icon: "โฌ…๏ธ",396                category: "Training",397                color: "#00ff00",398                description: "Chain rule and gradient computation"399            },400            {401                id: "regularization",402                title: "Regularization",403                icon: "๐Ÿ›ก๏ธ",404                category: "Training",405                color: "#00ff00",406                description: "L1/L2, Dropout, Early Stopping, Batch Norm"407            },408            {409                id: "batch-norm",410                title: "Batch Normalization",411                icon: "โš™๏ธ",412                category: "Training",413                color: "#00ff00",414                description: "Stabilizing and speeding up training"415            },416            // Module 2: Computer Vision Fundamentals417            {418                id: "cv-intro",419                title: "CV Fundamentals",420                icon: "๐Ÿ‘๏ธ",421                category: "Computer Vision",422                color: "#ff6b35",423                description: "Why ANNs fail with images, parameter explosion"424            },425            {426                id: "conv-layer",427                title: "Convolutional Layers",428                icon: "๐Ÿ–ผ๏ธ",429                category: "Computer Vision",430                color: "#ff6b35",431                description: "Kernels, filters, feature maps, stride, padding"432            },433            {434                id: "pooling",435                title: "Pooling Layers",436                icon: "๐Ÿ“ฆ",437                category: "Computer Vision",438                color: "#ff6b35",439                description: "Max pooling, average pooling, spatial reduction"440            },441            {442                id: "cnn-basics",443                title: "CNN Architecture",444                icon: "๐Ÿ—๏ธ",445                category: "Computer Vision",446                color: "#ff6b35",447                description: "Combining conv, pooling, and fully connected layers"448            },449            {450                id: "viz-filters",451                title: "Visualizing CNNs",452                icon: "๐Ÿ”",453                category: "Computer Vision",454                color: "#ff6b35",455                description: "What filters learn: edges โ†’ shapes โ†’ objects"456            },457            // Module 3: Advanced CNN Architectures458            {459                id: "lenet",460                title: "LeNet-5",461                icon: "๐Ÿ”ข",462                category: "CNN Architectures",463                color: "#ff00ff",464                description: "Classic digit recognizer (MNIST)"465            },466            {467                id: "alexnet",468                title: "AlexNet",469                icon: "๐ŸŒŸ",470                category: "CNN Architectures",471                color: "#ff00ff",472                description: "The breakthrough in deep computer vision (2012)"473            },474            {475                id: "vgg",476                title: "VGGNet",477                icon: "๐Ÿ“Š",478                category: "CNN Architectures",479                color: "#ff00ff",480                description: "VGG-16/19: Deep networks with small filters"481            },482            {483                id: "resnet",484                title: "ResNet",485                icon: "๐ŸŒ‰",486                category: "CNN Architectures",487                color: "#ff00ff",488                description: "Skip connections, solving vanishing gradients"489            },490            {491                id: "inception",492                title: "InceptionNet (GoogLeNet)",493                icon: "๐ŸŽฏ",494                category: "CNN Architectures",495                color: "#ff00ff",496                description: "1x1 convolutions, multi-scale feature extraction"497            },498            {499                id: "mobilenet",500                title: "MobileNet",501                icon: "๐Ÿ“ฑ",502                category: "CNN Architectures",503                color: "#ff00ff",504                description: "Depth-wise separable convolutions for efficiency"505            },506            {507                id: "transfer-learning",508                title: "Transfer Learning",509                icon: "๐Ÿ”„",510                category: "CNN Architectures",511                color: "#ff00ff",512                description: "Fine-tuning and leveraging pre-trained models"513            },514            // Module 4: Object Detection & Segmentation515            {516                id: "localization",517                title: "Object Localization",518                icon: "๐Ÿ“",519                category: "Detection",520                color: "#00ff00",521                description: "Bounding boxes and classification together"522            },523            {524                id: "rcnn",525                title: "R-CNN Family",526                icon: "๐ŸŽฏ",527                category: "Detection",528                color: "#00ff00",529                description: "R-CNN, Fast R-CNN, Faster R-CNN"530            },531            {532                id: "yolo",533                title: "YOLO",534                icon: "โšก",535                category: "Detection",536                color: "#00ff00",537                description: "Real-time object detection (v3, v5, v8)"538            },539            {540                id: "ssd",541                title: "SSD",542                icon: "๐Ÿš€",543                category: "Detection",544                color: "#00ff00",545                description: "Single Shot MultiBox Detector"546            },547            {548                id: "semantic-seg",549                title: "Semantic Segmentation",550                icon: "๐Ÿ–Œ๏ธ",551                category: "Segmentation",552                color: "#00ff00",553                description: "Pixel-level classification (U-Net)"554            },555            {556                id: "instance-seg",557                title: "Instance Segmentation",558                icon: "๐Ÿ‘ฅ",559                category: "Segmentation",560                color: "#00ff00",561                description: "Mask R-CNN and separate object instances"562            },563            {564                id: "face-recog",565                title: "Face Recognition",566                icon: "๐Ÿ‘ค",567                category: "Segmentation",568                color: "#00ff00",569                description: "Siamese networks and triplet loss"570            },571            // Module 5: Generative Models572            {573                id: "autoencoders",574                title: "Autoencoders",575                icon: "๐Ÿ”€",576                category: "Generative",577                color: "#ffaa00",578                description: "Encoder-decoder, latent space, denoising"579            },580            {581                id: "gans",582                title: "GANs (Generative Adversarial Networks)",583                icon: "๐ŸŽฎ",584                category: "Generative",585                color: "#ffaa00",586                description: "Generator vs. Discriminator, DCGAN"587            },588            {589                id: "diffusion",590                title: "Diffusion Models",591                icon: "๐ŸŒŠ",592                category: "Generative",593                color: "#ffaa00",594                description: "Foundation of Stable Diffusion and DALL-E"595            },596            // Additional Advanced Topics597            {598                id: "rnn",599                title: "RNNs & LSTMs",600                icon: "๐Ÿ”„",601                category: "Sequence",602                color: "#ff6b35",603                description: "Recurrent networks for sequential data"604            },605            {606                id: "transformers",607                title: "Transformers",608                icon: "๐Ÿ”—",609                category: "Sequence",610                color: "#ff6b35",611                description: "Attention mechanisms and modern architectures"612            },613            {614                id: "bert",615                title: "BERT & NLP Transformers",616                icon: "๐Ÿ“š",617                category: "NLP",618                color: "#ff6b35",619                description: "Bidirectional transformers for language"620            },621            {622                id: "gpt",623                title: "GPT & Language Models",624                icon: "๐Ÿ’ฌ",625                category: "NLP",626                color: "#ff6b35",627                description: "Autoregressive models and text generation"628            },629            {630                id: "vit",631                title: "Vision Transformers (ViT)",632                icon: "๐ŸŽจ",633                category: "Vision",634                color: "#ff6b35",635                description: "Transformers applied to image data"636            }637        ];638 639        function createModuleHTML(module) {640            return `641                <div class="module" id="${module.id}-module">642                    <button class="btn-back" onclick="switchTo('dashboard')">โ† Back to Dashboard</button>643                    <header>644                        <h1>${module.icon} ${module.title}</h1>645                        <p class="subtitle">${module.description}</p>646                    </header>647 648                    <div class="tabs">649                        <button class="tab-btn active" onclick="switchTab(event, '${module.id}-overview')">Overview</button>650                        <button class="tab-btn" onclick="switchTab(event, '${module.id}-concepts')">Key Concepts</button>651                        <button class="tab-btn" onclick="switchTab(event, '${module.id}-math')">Math</button>652                        <button class="tab-btn" onclick="switchTab(event, '${module.id}-applications')">Applications</button>653                        <button class="tab-btn" onclick="switchTab(event, '${module.id}-summary')">Summary</button>654                    </div>655 656                    <div id="${module.id}-overview" class="tab active">657                        <div class="section">658                            <h2>๐Ÿ“– Overview</h2>659                            <p>Complete coverage of ${module.title.toLowerCase()}. Learn the fundamentals, mathematics, real-world applications, and implementation details.</p>660                            <div class="info-box">661                                <div class="box-title">Learning Objectives</div>662                                <div class="box-content">663                                    โœ“ Understand core concepts and theory<br>664                                    โœ“ Master mathematical foundations<br>665                                    โœ“ Learn practical applications<br>666                                    โœ“ Implement and experiment667                                </div>668                            </div>669                        </div>670                    </div>671 672                    <div id="${module.id}-concepts" class="tab">673                        <div class="section">674                            <h2>๐ŸŽฏ Key Concepts</h2>675                            <p>Fundamental concepts and building blocks for ${module.title.toLowerCase()}.</p>676                            <div class="callout insight">677                                <div class="callout-title">๐Ÿ’ก Main Ideas</div>678                                This section covers the core ideas you need to understand before diving into mathematics.679                            </div>680                        </div>681                    </div>682 683                    <div id="${module.id}-math" class="tab">684                        <div class="section">685                            <h2>๐Ÿ“ Mathematical Foundation</h2>686                            <p>Rigorous mathematical treatment of ${module.title.toLowerCase()}.</p>687                            <div class="formula">688                                Mathematical formulas and derivations go here689                            </div>690                        </div>691                    </div>692 693                    <div id="${module.id}-applications" class="tab">694                        <div class="section">695                            <h2>๐ŸŒ Real-World Applications</h2>696                            <p>How ${module.title.toLowerCase()} is used in practice across different industries.</p>697                            <div class="info-box">698                                <div class="box-title">Use Cases</div>699                                <div class="box-content">700                                    Common applications and practical examples701                                </div>702                            </div>703                        </div>704                    </div>705 706                    <div id="${module.id}-summary" class="tab">707                        <div class="section">708                            <h2>โœ… Summary</h2>709                            <div class="info-box">710                                <div class="box-title">Key Takeaways</div>711                                <div class="box-content">712                                    โœ“ Essential concepts covered<br>713                                    โœ“ Mathematical foundations understood<br>714                                    โœ“ Real-world applications identified<br>715                                    โœ“ Ready for implementation716                                </div>717                            </div>718                        </div>719                    </div>720                </div>721            `;722        }723 724        function initDashboard() {725            const grid = document.getElementById("modulesGrid");726            const container = document.getElementById("modulesContainer");727 728            modules.forEach(module => {729                const card = document.createElement("div");730                card.className = "card";731                card.style.borderColor = module.color;732                card.onclick = () => switchTo(module.id + "-module");733                card.innerHTML = `734                    <div class="card-icon">${module.icon}</div>735                    <h3>${module.title}</h3>736                    <p>${module.description}</p>737                    <span class="category-label">${module.category}</span>738                `;739                grid.appendChild(card);740 741                const moduleHTML = createModuleHTML(module);742                container.innerHTML += moduleHTML;743            });744        }745 746        function switchTo(target) {747            document.querySelectorAll('.dashboard, .module').forEach(el => {748                el.classList.remove('active');749            });750            const elem = document.getElementById(target);751            if (elem) elem.classList.add('active');752        }753 754        function switchTab(e, tabId) {755            const module = e.target.closest('.module');756            if (!module) return;757            758            module.querySelectorAll('.tab').forEach(t => t.classList.remove('active'));759            module.querySelectorAll('.tab-btn').forEach(b => b.classList.remove('active'));760            761            const tab = document.getElementById(tabId);762            if (tab) tab.classList.add('active');763            e.target.classList.add('active');764        }765 766        initDashboard();767    </script>768</body>769</html>770