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https://github.com/chanmeng666/heat-flux-perceptrons-neural-networks
A comprehensive neural networks project combining theoretical understanding through manual implementation of feedforward networks with practical application in predicting heat influx for architectural design.
https://github.com/chanmeng666/heat-flux-perceptrons-neural-networks
architectural-engineering building-design deep-learning heat-flux-prediction machine-learning neural-networks python tensorflow thermal-analysis
Last synced: 8 days ago
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A comprehensive neural networks project combining theoretical understanding through manual implementation of feedforward networks with practical application in predicting heat influx for architectural design.
- Host: GitHub
- URL: https://github.com/chanmeng666/heat-flux-perceptrons-neural-networks
- Owner: ChanMeng666
- License: mit
- Created: 2024-10-05T02:23:35.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-12-16T03:12:11.000Z (18 days ago)
- Last Synced: 2024-12-16T04:22:43.108Z (18 days ago)
- Topics: architectural-engineering, building-design, deep-learning, heat-flux-prediction, machine-learning, neural-networks, python, tensorflow, thermal-analysis
- Language: Jupyter Notebook
- Homepage: https://github.com/ChanMeng666/heat-flux-perceptrons-neural-networks/blob/main/Assignment2_part1.ipynb
- Size: 1.18 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
Neural Networks: From Theory to Thermal Analysis 🏠
# 🌟 Features
### 🎓 Deep Learning from Ground Up
- Manual implementation of multi-layer feedforward networks
- Step-by-step visualization of backpropagation
- Detailed weight update calculations
- Example-by-example training process
### 🏗️ Heat Flux Prediction
- Multi-layer perceptron model for architectural applications
- Comparative analysis of different optimization techniques
- Real-world data analysis and visualization
- Performance evaluation across multiple metrics
### 📊 Comprehensive Analysis Tools
- Data exploration and visualization
- Multiple optimization strategies comparison
- Model performance evaluation
- Cross-validation and testing frameworks
# 🛠️ Technical Implementation
### Neural Network Components:
- Multi-layer perceptron architecture
- Sigmoid activation functions
- Gradient descent optimization
- Momentum and adaptive learning rate implementations
### Data Processing:
- MinMax scaling
- Train/validation/test splitting
- Feature engineering
- Performance metrics calculation
# 📦 Libraries Used
# 🚀 Getting Started
1. Clone the repository:
```bash
git clone https://github.com/ChanMeng666/heat-flux-perceptrons-neural-networks.git
```
2. Install required packages:
```bash
pip install -r requirements.txt
```
3. Run the Jupyter notebooks:
```bash
jupyter notebook
```
# 📊 Results
- Successful implementation of manually trained neural networks
- Comparative analysis of different optimization techniques
- Achieved high accuracy in heat flux predictions
- Comprehensive visualization of model performance
# 📖 Documentation
The project contains detailed Jupyter notebooks with:
- Theoretical explanations
- Step-by-step implementations
- Visualization of results
- Performance analysis
# 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
# 📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
# 📧 Contact
For questions or feedback, please open an issue in the repository.