Advanced Neural Network Applications

📋 Table of Contents

• Project Overview
• Features
• Datasets
• Implementation Details
• Getting Started
• Usage Examples
• Contributing
• 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 Model

The 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

def perceptron(inputs, weights, bias):
    # Model implementation details
    activation = np.dot(inputs, weights) + bias
    return 1 if activation > 0 else 0

Linear Neuron

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:

  pip install numpy pandas matplotlib scikit-learn

Installation

1. Clone the repository

   git clone https://github.com/ChanMeng666/advanced-neural-network-applications.git

2. Install dependencies

   pip install -r requirements.txt

3. Launch Jupyter Notebook

   jupyter notebook

💻 Usage Examples

Fish Classification

# 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

# 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 file for details.

🔧 Tech Stack

🙋‍♀ Author

Created and maintained by Chan Meng.