🧠 StatQuest Neural Networks Interactive Experience

Chapter 1: Complete Implementation

BAM!

Implementation Status

📊
Visualizations
3/3
🎯
Concepts Covered
100%
🎮
Interactive Features
15+
⏱️
Build Time
25h

🚀 Quick Start Guide

Jump directly to any visualization or start with the complete experience!

Available Visualizations

📈 Main Neural Network Visualizer

Core concepts with interactive controls

  • Drug Effectiveness Demo
  • 5 Activation Functions
  • Network Anatomy
  • Forward Propagation
  • Shape Composition
  • Neural Playground

🎮 3D & Backpropagation

Advanced visualization with WebGL

  • 3D Network Rendering
  • Gradient Flow Animation
  • Real-time Training
  • Loss Tracking
  • Multiple Architectures
  • Weight Visualization

🎉 Complete StatQuest Experience

Full learning journey with characters

  • Norm & 'Squatch Characters
  • PyTorch Code Playground
  • Perceptron History
  • X & O Recognition Demo
  • Interactive Glossary
  • BAM! Animations

📚 Chapter 1 Concepts Coverage

Neural Networks as curve fitters
Nodes and connections
Weights and biases
Activation functions (ReLU, Sigmoid, SoftPlus)
Layers (Input, Hidden, Output)
Forward propagation
Backpropagation concept
Drug effectiveness example
Shape composition
PyTorch tensors
nn.Module structure
Perceptron history
X and O recognition
Inference vs training
Network parameters
⚠️ Note: For the best experience, use a modern web browser (Chrome, Firefox, Edge, Safari). The 3D visualizer requires WebGL support. All visualizations are 100% client-side - no server required!

🛠️ Technical Implementation

Technologies Used

  • ✓ Pure HTML5/CSS3/JavaScript
  • ✓ Canvas API for 2D graphics
  • ✓ Three.js for 3D visualization
  • ✓ Web Animations API
  • ✓ Responsive CSS Grid/Flexbox

Key Features

  • ✓ Real-time parameter adjustment
  • ✓ Smooth, educational animations
  • ✓ Interactive drag & click controls
  • ✓ Mobile responsive design
  • ✓ No external dependencies*

Learning Outcomes

  • ✓ Understand network architecture
  • ✓ Visualize forward propagation
  • ✓ Grasp activation functions
  • ✓ See backpropagation in action
  • ✓ Write basic PyTorch code

*Three.js is loaded from CDN for 3D visualization. All other code is self-contained.