Complete walkthrough mode with educational features and slow training

walkthrough-enhancement.js

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+// 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.');