neat-edu 0.1.1

Interactive educational platform using NEAT neural networks for mathematical learning with real-time network visualization
docs.rs failed to build neat-edu-0.1.1
Please check the build logs for more information.
See Builds for ideas on how to fix a failed build, or Metadata for how to configure docs.rs builds.
If you believe this is docs.rs' fault, open an issue.

🧠 NEAT Educational Platform

Crates.io Documentation License: MIT

An interactive educational platform that uses NEAT (NeuroEvolution of Augmenting Topologies) neural networks to teach mathematics with real-time network visualization.

✨ Features

  • 🎯 Interactive Mathematical Problem Solving across multiple domains
  • 🧠 Real-time Neural Network Visualization showing AI reasoning
  • 📊 Performance Metrics with accuracy, efficiency, and complexity analysis
  • 🎲 Randomized Problem Generation for unlimited practice
  • 🎨 Beautiful Desktop GUI built with Tauri and TypeScript
  • 📚 Multiple Mathematical Topics: Arithmetic, Algebra, Calculus, Trigonometry, Statistics, Discrete Math

🚀 Quick Start

Installation

cargo install neat-edu

Running the Application

neat-edu

This will launch the desktop GUI application where you can:

  1. Select a mathematical topic (Arithmetic, Algebra, Calculus, etc.)
  2. Adjust difficulty level (Easy/Medium/Hard)
  3. Generate random problems and solve them interactively
  4. Visualize neural networks processing your solutions in real-time
  5. Track performance metrics and learning progress

🧮 Mathematical Domains

🔢 Arithmetic

  • Easy: Random addition problems (1-20)
  • Medium: Random multiplication problems (10-50 × 10-20)
  • Hard: Random division with decimal precision

🧮 Algebra

  • Easy: Linear equations like x + 7 = 15
  • Medium: Linear equations like 3x - 4 = 17
  • Hard: Quadratic equations like x² - 5x + 6 = 0

📈 Calculus

  • Easy: Polynomial derivatives like d/dx(x³) = 3x²
  • Medium: Complex derivatives with multiple terms
  • Hard: Integration problems with step-by-step solutions

📐 Trigonometry

  • Easy: Basic trig values like sin(30°) = 0.5
  • Medium: Trigonometric functions and identities
  • Hard: Solving trigonometric equations

📊 Statistics

  • Easy: Mean calculations with random datasets
  • Medium: Median finding with shuffled lists
  • Hard: Standard deviation calculations

🎲 Discrete Mathematics

  • Easy: Factorial and permutation problems
  • Medium: Combination calculations like C(n,r)
  • Hard: Set theory and combinatorial reasoning

🧠 Neural Network Visualization

Watch as NEAT neural networks evolve and adapt to solve mathematical problems:

  • Input Layer: Problem data encoding
  • Hidden Layers: Mathematical reasoning and pattern recognition
  • Output Layer: Solution generation and confidence
  • Real-time Metrics: Accuracy, efficiency, and network complexity

🛠️ Development

Prerequisites

  • Rust 1.75+
  • Node.js 18+ with npm
  • Tauri CLI

Building from Source

git clone https://github.com/your-username/neat-edu
cd neat-edu
npm install
cargo tauri dev

Project Structure

neat-edu/
├── src/
│   ├── main.rs              # Main application entry point
│   ├── problem_generator.rs # Randomized math problem generation
│   └── network_visualizer.rs # Neural network visualization
├── src/                     # Frontend TypeScript/HTML
├── Cargo.toml              # Rust dependencies and metadata
└── package.json            # Node.js dependencies

🎯 Educational Value

This platform demonstrates:

  • AI-Powered Learning: How neural networks can assist in education
  • Mathematical Reasoning: Step-by-step problem-solving approaches
  • Visual Learning: Network topology and mathematical pattern recognition
  • Adaptive Difficulty: Problems that scale with student ability
  • Interactive Feedback: Immediate validation and explanation

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. Areas for contribution:

  • New Mathematical Domains: Geometry, Number Theory, Linear Algebra
  • Enhanced Visualizations: 3D networks, animation, interactive exploration
  • Educational Features: Progress tracking, curriculum integration
  • Performance Optimizations: Faster problem generation, better validation

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🌟 Acknowledgments

  • NEAT Algorithm: Stanley & Miikkulainen for the foundational NEAT algorithm
  • Tauri Framework: For enabling cross-platform desktop applications
  • vis-network: For beautiful network visualization capabilities
  • Rust Community: For the exceptional tools and ecosystem

🔗 Links

🚀 Transform mathematical learning with the power of evolving neural networks!