- Manual implementation of multi-layer feedforward networks
- Step-by-step visualization of backpropagation
- Detailed weight update calculations
- Example-by-example training process
- Multi-layer perceptron model for architectural applications
- Comparative analysis of different optimization techniques
- Real-world data analysis and visualization
- Performance evaluation across multiple metrics
- Data exploration and visualization
- Multiple optimization strategies comparison
- Model performance evaluation
- Cross-validation and testing frameworks
- Multi-layer perceptron architecture
- Sigmoid activation functions
- Gradient descent optimization
- Momentum and adaptive learning rate implementations
- MinMax scaling
- Train/validation/test splitting
- Feature engineering
- Performance metrics calculation
- Clone the repository:
git clone https://github.com/ChanMeng666/heat-flux-perceptrons-neural-networks.git- Install required packages:
pip install -r requirements.txt- Run the Jupyter notebooks:
jupyter notebook- 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
The project contains detailed Jupyter notebooks with:
- Theoretical explanations
- Step-by-step implementations
- Visualization of results
- Performance analysis
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.
For questions or feedback, please open an issue in the repository.
Created and maintained by Chan Meng.
