Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/chanmeng666/advanced-neural-network-applications
【Stars make the code shine brighter! ⭐️】Educational project demonstrating practical applications of neural networks through perceptron-based fish classification and linear neuron heat influx prediction, implemented in Python with detailed Jupyter notebook examples and documentation.
https://github.com/chanmeng666/advanced-neural-network-applications
classification data-analysis jupyter-notebook linear-neuron machine-learning neural-networks perceptron python
Last synced: 3 days ago
JSON representation
【Stars make the code shine brighter! ⭐️】Educational project demonstrating practical applications of neural networks through perceptron-based fish classification and linear neuron heat influx prediction, implemented in Python with detailed Jupyter notebook examples and documentation.
- Host: GitHub
- URL: https://github.com/chanmeng666/advanced-neural-network-applications
- Owner: ChanMeng666
- License: mit
- Created: 2024-08-26T03:38:21.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-01-07T04:30:52.000Z (about 1 month ago)
- Last Synced: 2025-01-07T05:17:50.497Z (about 1 month ago)
- Topics: classification, data-analysis, jupyter-notebook, linear-neuron, machine-learning, neural-networks, perceptron, python
- Language: Jupyter Notebook
- Homepage: https://github.com/ChanMeng666/advanced-neural-network-applications/blob/main/Part1_1.ipynb
- Size: 594 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
Advanced Neural Network Applications
![]()
![]()
![]()
![]()
![]()
# 📋 Table of Contents
- [Project Overview](#-project-overview)
- [Features](#-features)
- [Datasets](#-datasets)
- [Implementation Details](#-implementation-details)
- [Getting Started](#-getting-started)
- [Usage Examples](#-usage-examples)
- [Contributing](#-contributing)
- [License](#-license)# 🔍 Project Overview
This repository demonstrates practical applications of neural network technologies through two distinct case studies:
1. Fish Classification using Perceptron Model
2. Heat Influx Prediction using Linear Neuron ModelThe project showcases how neural networks can be applied to real-world classification and prediction problems, with detailed implementations and analysis in Python.
# ⚡ Features
### 🎯 Perceptron Implementation
- Custom perceptron model for binary classification
- Detailed weight update visualization
- Classification boundary analysis
- Performance metrics calculation### 📈 Linear Neuron Model
- Single and multi-input implementations
- Batch learning demonstration
- 3D visualization of predictions
- Comprehensive error analysis### 📊 Data Analysis Tools
- Custom data visualization functions
- Performance metric calculations
- Model comparison utilities
- Interactive Jupyter notebooks# 📚 Datasets
### Fish Dataset
- Contains measurements of scale ring diameters
- Binary classification: Canadian vs Alaskan fish
- Features: freshwater and saltwater ring measurements
- Rich visualization of classification boundaries### Heat Influx Dataset
- Records heat influx measurements from building elevations
- Features: North and South elevation measurements
- Target: Heat influx predictions
- Includes 3D visualization capabilities# 🛠 Implementation Details
### Perceptron Model
```python
def perceptron(inputs, weights, bias):
# Model implementation details
activation = np.dot(inputs, weights) + bias
return 1 if activation > 0 else 0
```### Linear Neuron
```python
def linear_neuron(inputs, weights, bias):
# Model implementation details
return np.dot(inputs, weights) + bias
```# 🚀 Getting Started
### Prerequisites
- Python 3.x
- Jupyter Notebook
- Required packages:
```bash
pip install numpy pandas matplotlib scikit-learn
```### Installation
1. Clone the repository
```bash
git clone https://github.com/ChanMeng666/advanced-neural-network-applications.git
```
2. Install dependencies
```bash
pip install -r requirements.txt
```
3. Launch Jupyter Notebook
```bash
jupyter notebook
```# 💻 Usage Examples
### Fish Classification
```python
# Load and prepare data
fish_data = pd.read_csv('Fish_data.csv')# Train perceptron model
model = train_perceptron(fish_data)# Visualize results
plot_classification_boundary(model, fish_data)
```### Heat Influx Prediction
```python
# Load and prepare data
heat_data = pd.read_csv('heat_influx_noth_south.csv')# Train linear neuron
model = train_linear_neuron(heat_data)# Visualize predictions
plot_3d_predictions(model, heat_data)
```# 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.
# 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.# 🔧 Tech Stack





## 🙋♀ Author
Created and maintained by [Chan Meng](https://github.com/ChanMeng666).