Delete walkthrough-enhancement.js

walkthrough-enhancement.js

@@ -1,755 +0,0 @@
-// Complete Walkthrough Mode Implementation
-// This replaces the incomplete walkthrough section in index.html
-
-// Walkthrough Mode functionality
-let walkthroughActive = false;
-let walkthroughStep = 0;
-let walkthroughTutorial = null;
-let walkthroughTrainingSpeed = 2000; // Much slower training speed for walkthrough
-let walkthroughInterval = null;
-let walkthroughExplanationMode = false;
-
-const walkthroughTutorials = {
-    basics: {
-        title: 'Neural Network Basics',
-        task: 'and', // Use AND gate for demonstration
-        steps: [
-            {
-                title: 'Welcome to Neural Networks! 🧠',
-                content: 'Neural networks are inspired by the human brain. They consist of layers of interconnected neurons that process information. We\'ll use a simple AND gate to learn how they work step by step!',
-                element: null,
-                position: 'center',
-                action: 'start'
-            },
-            {
-                title: 'Input Layer (Blue Circles)',
-                content: 'These blue circles are input neurons. For our AND gate, we have 2 inputs that can be either 0 or 1. Watch the numbers inside - they show the current input values being processed!',
-                element: '#networkCanvas',
-                position: 'right',
-                highlight: {x: 50, y: 50, width: 100, height: 200},
-                action: 'highlight_inputs'
-            },
-            {
-                title: 'Hidden Layer (Green Circles)',
-                content: 'These green neurons are the "thinking" layer. They receive signals from inputs, multiply them by weights, add biases, and decide how "excited" to get. The numbers show their activation levels!',
-                element: '#networkCanvas',
-                position: 'left',
-                highlight: {x: 150, y: 50, width: 100, height: 200},
-                action: 'highlight_hidden'
-            },
-            {
-                title: 'Output Layer (Purple Circle)',
-                content: 'This purple neuron gives us the final answer! For AND gate, it should output 1 only when BOTH inputs are 1. Right now it\'s making random guesses - that\'s why we need to train it!',
-                element: '#networkCanvas',
-                position: 'left',
-                highlight: {x: 250, y: 120, width: 100, height: 60},
-                action: 'highlight_output'
-            },
-            {
-                title: 'Connections (Colored Lines)',
-                content: 'These lines are connections with "weights". Green lines are positive (excite the next neuron), red lines are negative (inhibit it). Thicker lines = stronger connections. The AI learns by adjusting these!',
-                element: '#networkCanvas',
-                position: 'right',
-                highlight: {x: 0, y: 0, width: 400, height: 300},
-                action: 'highlight_weights'
-            },
-            {
-                title: 'Training Data',
-                content: 'Here\'s our training data! Each card shows: input values → expected output. The AI will see these examples and learn the AND gate pattern. Notice the "Raw" prediction - it starts random!',
-                element: '#taskOutput',
-                position: 'top',
-                highlight: {x: -10, y: -10, width: 'auto', height: 'auto'},
-                action: 'highlight_data'
-            },
-            {
-                title: 'Let\'s Start Training!',
-                content: 'Now I\'ll start training VERY slowly so you can see every step. Watch how the neurons\' values change, connections strengthen/weaken, and the loss decreases as the AI learns!',
-                element: '#trainBtn',
-                position: 'top',
-                highlight: {x: -5, y: -5, width: 'auto', height: 'auto'},
-                action: 'start_training'
-            }
-        ]
-    },
-    training: {
-        title: 'How Training Works',
-        task: 'xor', // Use XOR for complexity
-        steps: [
-            {
-                title: 'Welcome to AI Training! 🎯',
-                content: 'Training is how AI learns! We\'ll use the XOR gate - a challenging problem that requires the AI to think in complex ways. Let\'s see how the network learns step by step.',
-                element: null,
-                position: 'center',
-                action: 'start'
-            },
-            {
-                title: 'Forward Propagation',
-                content: 'Step 1: Forward pass! The input flows through the network like water through pipes. Each neuron receives inputs, multiplies by weights, adds bias, and applies activation. Watch the flow!',
-                element: '#networkCanvas',
-                position: 'right',
-                highlight: {x: 0, y: 0, width: 400, height: 300},
-                action: 'demo_forward'
-            },
-            {
-                title: 'Making a Prediction',
-                content: 'The output neuron gives us a prediction! For XOR: 0,0→0, 0,1→1, 1,0→1, 1,1→0. Right now it\'s wrong - see the red "Wrong" status. The raw output should be close to the target!',
-                element: '#taskOutput',
-                position: 'top',
-                highlight: {x: -10, y: -10, width: 'auto', height: 'auto'},
-                action: 'show_prediction'
-            },
-            {
-                title: 'Calculating Loss',
-                content: 'Loss measures how wrong we are! It\'s the difference between prediction and target, squared. High loss = very wrong. Low loss = very right. Watch this number in the stats panel!',
-                element: '#lossValue',
-                position: 'bottom',
-                highlight: {x: -10, y: -10, width: 'auto', height: 'auto'},
-                action: 'show_loss'
-            },
-            {
-                title: 'Backpropagation Magic',
-                content: 'Step 2: Backward pass! The AI traces back through the network asking "who\'s responsible for this error?" and adjusts weights accordingly. This is backpropagation - the heart of deep learning!',
-                element: '#networkCanvas',
-                position: 'left',
-                highlight: {x: 0, y: 0, width: 400, height: 300},
-                action: 'demo_backward'
-            },
-            {
-                title: 'Weight Updates',
-                content: 'The AI nudges each weight slightly in the direction that reduces error. Good connections get stronger, bad ones get weaker. Watch the line colors and thickness change!',
-                element: '#networkCanvas',
-                position: 'right',
-                highlight: {x: 0, y: 0, width: 400, height: 300},
-                action: 'show_weight_updates'
-            },
-            {
-                title: 'Learning Progress',
-                content: 'Each training cycle is called an "epoch". Watch the loss chart - it should generally go down as the AI gets better! Sometimes it goes up temporarily - that\'s normal during learning.',
-                element: '#lossChart',
-                position: 'right',
-                highlight: {x: -10, y: -10, width: 'auto', height: 'auto'},
-                action: 'show_progress'
-            },
-            {
-                title: 'Continuous Learning',
-                content: 'Now watch it train continuously! Each step: forward pass → calculate loss → backward pass → update weights → repeat. This is how all AI learns, from image recognition to chatbots!',
-                element: null,
-                position: 'center',
-                action: 'continuous_training'
-            }
-        ]
-    },
-    visualization: {
-        title: 'Understanding the Visualizations',
-        task: 'classification', // Use 2D classification for visual demo
-        steps: [
-            {
-                title: 'Reading AI Visualizations 📊',
-                content: 'Visualizations help us understand what\'s happening inside AI! We\'ll use 2D classification - separating red and blue points in space - to see how neural networks think.',
-                element: null,
-                position: 'center',
-                action: 'start'
-            },
-            {
-                title: 'Network Diagram - The Brain Map',
-                content: 'This shows the AI\'s structure! Circles = neurons (processing units), Lines = connections (information pathways). Colors show activity levels: bright = excited, dark = calm.',
-                element: '#networkCanvas',
-                position: 'right',
-                highlight: {x: 0, y: 0, width: 400, height: 300},
-                action: 'explain_network'
-            },
-            {
-                title: 'Data Visualization - The Problem Space',
-                content: 'This 2D plot shows our data! Red dots should be classified as 0, blue dots as 1. The AI needs to learn an invisible boundary that separates them correctly.',
-                element: '#dataViz',
-                position: 'left',
-                highlight: {x: -10, y: -10, width: 'auto', height: 'auto'},
-                action: 'explain_data_viz'
-            },
-            {
-                title: 'Training Progress Chart',
-                content: 'The loss chart shows learning over time! Starting high (confused), gradually decreasing (getting smarter). Flat periods mean it\'s thinking, sharp drops mean breakthroughs!',
-                element: '#lossChart',
-                position: 'right',
-                highlight: {x: -10, y: -10, width: 'auto', height: 'auto'},
-                action: 'explain_loss_chart'
-            },
-            {
-                title: 'Prediction Cards - The Report Card',
-                content: 'Each card shows: the input → expected output. "Raw" = neuron\'s actual output (0-1), "Predicted" = final decision, Status = right/wrong. Green borders = correct predictions!',
-                element: '#taskOutput',
-                position: 'top',
-                highlight: {x: -10, y: -10, width: 'auto', height: 'auto'},
-                action: 'explain_predictions'
-            },
-            {
-                title: 'Stats Panel - The Dashboard',
-                content: 'Epochs = training cycles completed, Loss = how wrong we are, Accuracy = % correct predictions, Current = which example we\'re processing now. These tell the whole story!',
-                element: '.stats-grid',
-                position: 'bottom',
-                highlight: {x: -10, y: -10, width: 'auto', height: 'auto'},
-                action: 'explain_stats'
-            },
-            {
-                title: 'Watching It All Together',
-                content: 'Now watch everything update in harmony! Network neurons fire, data points get classified, loss decreases, accuracy increases. You\'re seeing the birth of artificial intelligence!',
-                element: null,
-                position: 'center',
-                action: 'full_demo'
-            }
-        ]
-    },
-    logic: {
-        title: 'Logic Gates Deep Dive',
-        task: 'xor', // Start with XOR as the most interesting
-        steps: [
-            {
-                title: 'Logic Gates - AI\'s Building Blocks 🔗',
-                content: 'Logic gates are the foundation of all computing! We\'ll explore how neural networks learn AND, OR, and the famous XOR gate - the problem that sparked the deep learning revolution!',
-                element: null,
-                position: 'center',
-                action: 'start'
-            },
-            {
-                title: 'The XOR Challenge',
-                content: 'XOR (exclusive or) outputs 1 when inputs are different. Seems simple, but it stumped early AI for decades! It\'s not "linearly separable" - you can\'t draw a straight line to separate the outputs.',
-                element: '#taskOutput',
-                position: 'top',
-                highlight: {x: -10, y: -10, width: 'auto', height: 'auto'},
-                action: 'explain_xor'
-            },
-            {
-                title: 'Why XOR Needs Deep Networks',
-                content: 'Look at our network: 2→12→8→1. We need these hidden layers! Each layer transforms the data, and together they can solve XOR. Single-layer networks can\'t do this!',
-                element: '#networkCanvas',
-                position: 'right',
-                highlight: {x: 0, y: 0, width: 400, height: 300},
-                action: 'explain_depth'
-            },
-            {
-                title: 'Layer 1: Feature Detection',
-                content: 'The first hidden layer (12 neurons) learns to detect patterns in the input. Some neurons might learn "both inputs high", others "both inputs low", etc. These become building blocks!',
-                element: '#networkCanvas',
-                position: 'left',
-                highlight: {x: 100, y: 50, width: 80, height: 200},
-                action: 'explain_layer1'
-            },
-            {
-                title: 'Layer 2: Combination Logic',
-                content: 'The second hidden layer (8 neurons) combines the features from layer 1. It might learn rules like "if feature A is active but feature B isn\'t, then activate". This creates complex logic!',
-                element: '#networkCanvas',
-                position: 'right',
-                highlight: {x: 200, y: 80, width: 80, height: 140},
-                action: 'explain_layer2'
-            },
-            {
-                title: 'Output: The Final Decision',
-                content: 'The output neuron combines all the complex features into a final decision. It learns to say "yes" (1) when the XOR pattern is detected, "no" (0) otherwise. Magic!',
-                element: '#networkCanvas',
-                position: 'left',
-                highlight: {x: 300, y: 120, width: 80, height: 60},
-                action: 'explain_output'
-            },
-            {
-                title: 'Training Process',
-                content: 'Watch as the network slowly learns XOR! Early on, it makes random guesses. Gradually, it discovers the pattern. You\'ll see the accuracy climb from 25% (random) to 100% (perfect)!',
-                element: '#accuracyValue',
-                position: 'bottom',
-                highlight: {x: -10, y: -10, width: 'auto', height: 'auto'},
-                action: 'demo_training'
-            },
-            {
-                title: 'The Learning Moment',
-                content: 'There\'s often a "eureka moment" where the AI suddenly "gets it" - loss drops rapidly, accuracy jumps! This is the network discovering the XOR pattern. It\'s like watching intelligence emerge!',
-                element: '#lossChart',
-                position: 'right',
-                highlight: {x: -10, y: -10, width: 'auto', height: 'auto'},
-                action: 'show_breakthrough'
-            }
-        ]
-    }
-};
-
-// Walkthrough DOM elements
-const walkthroughOverlay = document.getElementById('walkthroughOverlay');
-const walkthroughHighlight = document.getElementById('walkthroughHighlight');
-const walkthroughPopup = document.getElementById('walkthroughPopup');
-const walkthroughTitle = document.getElementById('walkthroughTitle');
-const walkthroughContent = document.getElementById('walkthroughContent');
-const walkthroughProgress = document.getElementById('walkthroughProgress');
-const walkthroughStepSpan = document.getElementById('walkthroughStep');
-const walkthroughTotal = document.getElementById('walkthroughTotal');
-const walkthroughIndicator = document.getElementById('walkthroughIndicator');
-const walkthroughPrev = document.getElementById('walkthroughPrev');
-const walkthroughNext = document.getElementById('walkthroughNext');
-const walkthroughSkip = document.getElementById('walkthroughSkip');
-
-// Start walkthrough function
-function startWalkthrough(tutorialId) {
-    const tutorial = walkthroughTutorials[tutorialId];
-    if (!tutorial) return;
-
-    walkthroughActive = true;
-    walkthroughTutorial = tutorial;
-    walkthroughStep = 0;
-    walkthroughExplanationMode = true;
-
-    // Load the tutorial task if specified
-    if (tutorial.task) {
-        currentCategory = 'fundamentals'; // Most tutorials use fundamentals
-        selectTask(tutorial.task);
-        // Wait a moment for task to load
-        setTimeout(() => {
-            showWalkthroughStep();
-        }, 500);
-    } else {
-        showWalkthroughStep();
-    }
-
-    // Hide walkthrough mode menu
-    document.getElementById('walkthroughMode').style.display = 'none';
-    
-    // Show indicator
-    walkthroughIndicator.style.display = 'block';
-}
-
-// Show current walkthrough step
-function showWalkthroughStep() {
-    if (!walkthroughTutorial || walkthroughStep >= walkthroughTutorial.steps.length) {
-        endWalkthrough();
-        return;
-    }
-
-    const step = walkthroughTutorial.steps[walkthroughStep];
-    
-    // Update progress
-    walkthroughProgress.style.display = 'block';
-    walkthroughStepSpan.textContent = walkthroughStep + 1;
-    walkthroughTotal.textContent = walkthroughTutorial.steps.length;
-
-    // Update popup content
-    walkthroughTitle.textContent = step.title;
-    walkthroughContent.textContent = step.content;
-
-    // Handle special actions
-    if (step.action) {
-        executeWalkthroughAction(step.action);
-    }
-
-    // Position popup and highlight
-    if (step.element) {
-        positionWalkthroughElements(step);
-    } else {
-        // Center popup for intro steps
-        centerWalkthroughPopup();
-        walkthroughHighlight.style.display = 'none';
-    }
-
-    // Show overlay and popup
-    walkthroughOverlay.style.display = 'block';
-    walkthroughPopup.style.display = 'block';
-
-    // Update buttons
-    walkthroughPrev.style.display = walkthroughStep > 0 ? 'block' : 'none';
-    walkthroughNext.textContent = walkthroughStep === walkthroughTutorial.steps.length - 1 ? 'Finish' : 'Next';
-}
-
-// Position walkthrough elements
-function positionWalkthroughElements(step) {
-    const element = document.querySelector(step.element);
-    if (!element) return;
-
-    const rect = element.getBoundingClientRect();
-    const popup = walkthroughPopup;
-    
-    // Position highlight
-    if (step.highlight) {
-        walkthroughHighlight.style.display = 'block';
-        if (step.highlight.width === 'auto') {
-            walkthroughHighlight.style.left = (rect.left - 10) + 'px';
-            walkthroughHighlight.style.top = (rect.top - 10) + 'px';
-            walkthroughHighlight.style.width = (rect.width + 20) + 'px';
-            walkthroughHighlight.style.height = (rect.height + 20) + 'px';
-        } else {
-            walkthroughHighlight.style.left = (rect.left + step.highlight.x) + 'px';
-            walkthroughHighlight.style.top = (rect.top + step.highlight.y) + 'px';
-            walkthroughHighlight.style.width = step.highlight.width + 'px';
-            walkthroughHighlight.style.height = step.highlight.height + 'px';
-        }
-    } else {
-        walkthroughHighlight.style.display = 'none';
-    }
-
-    // Position popup based on position preference
-    popup.className = 'walkthrough-popup ' + step.position;
-    
-    const popupRect = popup.getBoundingClientRect();
-    let left, top;
-
-    switch (step.position) {
-        case 'top':
-            left = rect.left + rect.width / 2 - popupRect.width / 2;
-            top = rect.top - popupRect.height - 20;
-            break;
-        case 'bottom':
-            left = rect.left + rect.width / 2 - popupRect.width / 2;
-            top = rect.bottom + 20;
-            break;
-        case 'left':
-            left = rect.left - popupRect.width - 20;
-            top = rect.top + rect.height / 2 - popupRect.height / 2;
-            break;
-        case 'right':
-            left = rect.right + 20;
-            top = rect.top + rect.height / 2 - popupRect.height / 2;
-            break;
-        default:
-            centerWalkthroughPopup();
-            return;
-    }
-
-    // Keep popup on screen
-    left = Math.max(10, Math.min(left, window.innerWidth - popupRect.width - 10));
-    top = Math.max(10, Math.min(top, window.innerHeight - popupRect.height - 10));
-
-    popup.style.left = left + 'px';
-    popup.style.top = top + 'px';
-}
-
-// Center walkthrough popup
-function centerWalkthroughPopup() {
-    const popup = walkthroughPopup;
-    popup.className = 'walkthrough-popup center';
-    popup.style.left = '50%';
-    popup.style.top = '50%';
-    popup.style.transform = 'translate(-50%, -50%)';
-}
-
-// Execute special walkthrough actions
-function executeWalkthroughAction(action) {
-    switch (action) {
-        case 'start':
-            // Reset network and prepare for demonstration
-            if (currentTask && network) {
-                reset();
-            }
-            break;
-            
-        case 'start_training':
-            // Start very slow training for educational purposes
-            if (!isTraining && currentTask) {
-                trainBtn.click();
-            }
-            break;
-            
-        case 'demo_forward':
-            // Slow down even more to show forward propagation
-            walkthroughTrainingSpeed = 3000;
-            break;
-            
-        case 'demo_backward':
-            // Highlight the backward pass concept
-            addWalkthroughExplanation('Backpropagation traces the error backwards through each connection, calculating how much each weight contributed to the mistake.');
-            break;
-            
-        case 'show_weight_updates':
-            // Highlight weight changes
-            addWalkthroughExplanation('Watch the connection lines change! Thicker lines = stronger weights, colors show positive (green) vs negative (red) influence.');
-            break;
-            
-        case 'continuous_training':
-            // Return to normal-ish speed but still educational
-            walkthroughTrainingSpeed = 1000;
-            break;
-            
-        case 'explain_network':
-            // Add network explanation overlay
-            addWalkthroughExplanation('Bright neurons are highly activated (excited), dark neurons are inactive (calm). The patterns show how information flows!');
-            break;
-            
-        case 'explain_xor':
-            // Special explanation for XOR challenge
-            addWalkthroughExplanation('XOR is special! Unlike AND/OR, you cannot draw a single straight line to separate the correct outputs. This requires complex thinking!');
-            break;
-            
-        case 'demo_training':
-            // Start training and monitor for breakthrough moments
-            if (!isTraining && currentTask) {
-                trainBtn.click();
-                monitorTrainingProgress();
-            }
-            break;
-    }
-}
-
-// Add temporary explanation overlay
-function addWalkthroughExplanation(text) {
-    const explanation = document.createElement('div');
-    explanation.style.cssText = `
-        position: fixed;
-        bottom: 100px;
-        left: 50%;
-        transform: translateX(-50%);
-        background: rgba(16, 185, 129, 0.95);
-        color: white;
-        padding: 1rem 2rem;
-        border-radius: 0.5rem;
-        font-size: 0.9rem;
-        max-width: 400px;
-        text-align: center;
-        z-index: 10003;
-        animation: fadeInOut 4s ease-in-out;
-    `;
-    explanation.textContent = text;
-    document.body.appendChild(explanation);
-    
-    setTimeout(() => {
-        if (explanation.parentNode) {
-            explanation.parentNode.removeChild(explanation);
-        }
-    }, 4000);
-}
-
-// Add CSS animation for explanations
-const walkthroughStyle = document.createElement('style');
-walkthroughStyle.textContent = `
-    @keyframes fadeInOut {
-        0% { opacity: 0; transform: translateX(-50%) translateY(20px); }
-        10% { opacity: 1; transform: translateX(-50%) translateY(0); }
-        90% { opacity: 1; transform: translateX(-50%) translateY(0); }
-        100% { opacity: 0; transform: translateX(-50%) translateY(-20px); }
-    }
-`;
-document.head.appendChild(walkthroughStyle);
-
-// Monitor training for educational moments
-function monitorTrainingProgress() {
-    if (!walkthroughActive) return;
-    
-    const checkProgress = () => {
-        if (!isTraining || !walkthroughActive) return;
-        
-        // Look for breakthrough moments (rapid loss decrease)
-        if (lossHistory.length > 10) {
-            const recent = lossHistory.slice(-5);
-            const older = lossHistory.slice(-10, -5);
-            const recentAvg = recent.reduce((a, b) => a + b) / recent.length;
-            const olderAvg = older.reduce((a, b) => a + b) / older.length;
-            
-            if (olderAvg > 0.5 && recentAvg < 0.1) {
-                addWalkthroughExplanation('🎉 Breakthrough moment! The AI just discovered the pattern - watch the loss plummet!');
-            }
-        }
-        
-        // Check for high accuracy achievements
-        if (accuracy > 0.9 && epoch > 50) {
-            addWalkthroughExplanation('🎯 Excellent! The AI has mastered this pattern with over 90% accuracy!');
-        }
-        
-        setTimeout(checkProgress, 2000);
-    };
-    
-    setTimeout(checkProgress, 5000);
-}
-
-// Navigation functions
-function nextWalkthroughStep() {
-    walkthroughStep++;
-    showWalkthroughStep();
-}
-
-function prevWalkthroughStep() {
-    if (walkthroughStep > 0) {
-        walkthroughStep--;
-        showWalkthroughStep();
-    }
-}
-
-function endWalkthrough() {
-    walkthroughActive = false;
-    walkthroughTutorial = null;
-    walkthroughStep = 0;
-    walkthroughExplanationMode = false;
-    walkthroughTrainingSpeed = 2000; // Reset to slow but not too slow
-
-    // Hide walkthrough UI
-    walkthroughOverlay.style.display = 'none';
-    walkthroughPopup.style.display = 'none';
-    walkthroughHighlight.style.display = 'none';
-    walkthroughProgress.style.display = 'none';
-    walkthroughIndicator.style.display = 'none';
-
-    // Clear any intervals
-    if (walkthroughInterval) {
-        clearInterval(walkthroughInterval);
-        walkthroughInterval = null;
-    }
-
-    // Show completion message
-    addWalkthroughExplanation('🎓 Walkthrough complete! You now understand neural networks better. Try exploring other tasks!');
-
-    // Return to normal training speed when walkthrough ends
-    setTimeout(() => {
-        walkthroughTrainingSpeed = 100; // Back to normal speed
-    }, 5000);
-}
-
-// Event listeners for walkthrough
-walkthroughNext.addEventListener('click', nextWalkthroughStep);
-walkthroughPrev.addEventListener('click', prevWalkthroughStep);
-walkthroughSkip.addEventListener('click', endWalkthrough);
-
-// Keyboard navigation for walkthrough
-document.addEventListener('keydown', (e) => {
-    if (!walkthroughActive) return;
-    
-    if (e.key === 'ArrowRight' || e.key === ' ') {
-        e.preventDefault();
-        nextWalkthroughStep();
-    } else if (e.key === 'ArrowLeft') {
-        e.preventDefault();
-        prevWalkthroughStep();
-    } else if (e.key === 'Escape') {
-        e.preventDefault();
-        endWalkthrough();
-    }
-});
-
-// Enhanced training step for walkthrough mode
-function walkthroughTrainStep() {
-    if (!walkthroughActive) {
-        trainStep(); // Use normal training
-        return;
-    }
-
-    // Detailed step-by-step training for education
-    const sample = currentTask.data[currentSample];
-    
-    // 1. Forward propagation with detailed explanation
-    const output = network.forward(sample.input);
-    activations = [...network.activations];
-    
-    if (walkthroughExplanationMode && epoch % 10 === 0) {
-        const explanation = `Step ${epoch}: Processing ${sample.label}. ` +
-            `Input: [${sample.input.join(', ')}] → ` +
-            `Hidden activations → ` +
-            `Output: ${output[0].toFixed(3)} (target: ${sample.target[0]})`;
-        console.log(explanation); // For developers who open console
-    }
-
-    // 2. Calculate loss with explanation
-    const result = network.trainBatch([sample]);
-    currentLoss = result.loss;
-    weightChanges = result.weightChanges;
-    
-    // 3. Update tracking variables
-    lossHistory.push(result.loss);
-    if (lossHistory.length > 100) lossHistory.shift();
-    
-    // 4. Calculate predictions for all samples
-    const newPredictions = currentTask.data.map(data => {
-        const output = network.forward(data.input);
-        const rawOutput = output[0];
-        
-        let predicted, correct;
-        if (currentTask.isRegression) {
-            predicted = rawOutput.toFixed(2);
-            let tolerance = 0.1;
-            
-            if (currentTask.title.includes('Autoencoder')) {
-                tolerance = 0.2;
-                const allOutputs = network.forward(data.input);
-                let totalError = 0;
-                for (let j = 0; j < data.target.length; j++) {
-                    totalError += Math.abs(allOutputs[j] - data.target[j]);
-                }
-                const avgError = totalError / data.target.length;
-                correct = avgError < tolerance;
-            } else {
-                correct = Math.abs(rawOutput - data.target[0]) < tolerance;
-            }
-        } else {
-            predicted = rawOutput >= 0.5 ? 1 : 0;
-            const target = data.target[0];
-            correct = predicted === target;
-        }
-        
-        return {
-            ...data,
-            output: rawOutput,
-            predicted: predicted,
-            correct: correct
-        };
-    });
-    predictions = newPredictions;
-    
-    // 5. Update metrics
-    const correct = newPredictions.filter(p => p.correct).length;
-    accuracy = correct / newPredictions.length;
-    
-    const totalLoss = newPredictions.reduce((sum, p) => 
-        sum + Math.pow(p.target[0] - p.output, 2), 0) / newPredictions.length;
-    avgLoss = totalLoss;
-    
-    currentSample = (currentSample + 1) % currentTask.data.length;
-    epoch++;
-    
-    // 6. Update UI
-    updateUI();
-    
-    // 7. Special walkthrough feedback
-    if (walkthroughActive && epoch % 20 === 0) {
-        const accuracyPercent = (accuracy * 100).toFixed(1);
-        if (accuracy > 0.8) {
-            addWalkthroughExplanation(`🎉 Great progress! ${accuracyPercent}% accuracy - the AI is learning the pattern!`);
-        } else if (accuracy > 0.5) {
-            addWalkthroughExplanation(`📈 Learning... ${accuracyPercent}% accuracy. Watch the weights adapt!`);
-        }
-    }
-}
-
-// Override the original trainStep function when in walkthrough mode
-const originalTrainStep = trainStep;
-trainStep = function() {
-    if (walkthroughActive) {
-        walkthroughTrainStep();
-    } else {
-        originalTrainStep();
-    }
-};
-
-// Override training button behavior for walkthrough mode
-const originalTrainBtnClick = trainBtn.onclick;
-trainBtn.addEventListener('click', (e) => {
-    if (walkthroughActive) {
-        // Use much slower speed in walkthrough mode
-        isTraining = !isTraining;
-        
-        if (isTraining) {
-            trainBtn.innerHTML = `
-                <svg class="icon" fill="currentColor" viewBox="0 0 24 24">
-                    <path d="M6 19h4V5H6v14zm8-14v14h4V5h-4z"/>
-                </svg>
-                Pause Training
-            `;
-            trainBtn.className = 'btn btn-pause';
-            
-            trainInterval = setInterval(trainStep, walkthroughTrainingSpeed);
-        } else {
-            trainBtn.innerHTML = `
-                <svg class="icon" fill="currentColor" viewBox="0 0 24 24">
-                    <path d="M8 5v14l11-7z"/>
-                </svg>
-                Start Training
-            `;
-            trainBtn.className = 'btn btn-start';
-            
-            clearInterval(trainInterval);
-        }
-        
-        e.stopPropagation();
-        return false;
-    }
-});
-
-console.log('🎓 Walkthrough mode enhanced! Users can now learn step-by-step how neural networks work.');