🧠 Neural Network Visual Builder

Build, train, and understand neural networks visually — no coding required.

Neural Network Visual Builder is an open-source desktop and web application designed to democratize machine learning. By providing a drag-and-drop interface, it allows anyone to architect complex models, visualize data flow in real-time, and export their creations into production-ready code.

---

🌐 Live Demo

• 👉 Web App: https://your-app-link.com
• 👉 Download Desktop App: (Coming Soon)

---

📸 Screenshot

---

🚀 About The Project

The goal of this project is to bridge the gap between conceptual AI and functional code.

🎯 Key Objectives

• Accessibility: Enable non-coders to build and train neural networks.
• Education: Help students visualize how data transforms through weights and biases.
• Efficiency: Allow developers to prototype architectures 10x faster than writing manual boilerplate.
• Collaboration: Enable teams to observe training metrics and gradients in a shared visual space.

---

✨ Features & Roadmap

We are currently in active development. Track our progress below:

Feature	Status
Create a new neural network in-browser	✅
Visualize network architecture & connections	✅
Real-time token/data flow animation (see how data moves through each layer step-by-step)	✖
Pause execution at any specific layer (inspect intermediate outputs during forward pass)	✖
Inspect activations of layers/neurons (view tensor values, distributions, and shapes)	✖
Collapse repeated layers for clean UI (e.g., group blocks like Linear × N)	✖
Manually edit weights and biases (fine-tune or experiment with parameters directly)	✖
Swap FFN with Mixture-of-Experts (MoE) blocks (advanced modular architectures)	✖
Export to PyTorch code (generate ready-to-run Python nn.Module)	✖
Export to TensorFlow / Keras equivalents (multi-framework model generation)	✖
Real-time training graphs (live loss, accuracy, and metrics visualization)	✖
Training statistics dashboard (min/max/mean, gradients, weight distributions)	✖
Live training visualization (watch weights and activations evolve during training)	✖

🧩 Planned Enhancements

• Model Import: Convert existing PyTorch .pth files into visual graphs.
• Dataset Integration: Drag-and-drop CSVs or image folders for training.
• Plugin System: Support for custom community-made layers.
• Multi-Backend: Support for TensorFlow and ONNX export.

---

🛠️ Tech Stack

• Frontend: Vue.js / Nuxt.js
• Backend: Node.js & Rust
• ML Engine: TensorFlow.js (Browser) / PyTorch (Export)
• Desktop App: Electron

---

📦 Installation

To get a local copy up and running, follow these simple steps:

# Clone the repository
git clone https://github.com/Godwinss24/neural-network-builder.git

# Navigate into the directory
cd neural-network-builder

# Install dependencies
npm install

# Start the development server
npm run dev

---

🤝 Contributing

Contributions make the open-source community an amazing place to learn and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

---

📄 License

Distributed under the MIT License. See LICENSE for more information.

---

🌟 Support

If you find this project helpful, please consider:

• Giving it a ⭐ on GitHub.
• Sharing it with your peers.
• Reporting bugs or suggesting features via Issues.

---

🙌 Acknowledgements

• TensorFlow.js
• PyTorch
• Nuxt
• The global Open-Source ML community