Deep Learning Projects Repository 🧠

A collection of deep learning implementations covering fundamental concepts and practical applications.

📂 Repository Structure

1. California Housing Price Prediction

Path: /California-Housing
Description:
Predicts median house values in California using neural networks with:

• Data exploration and visualization
• Feature engineering
• Sequential model architecture
• Performance evaluation

Key Files:

• data_processing.py - Data loading and preprocessing
• model.py - Neural network implementation
• training.py - Model training pipeline
• visualization.py - EDA and results plotting

2. Fashion MNIST Classification

Path: /Fashion-MNIST
Description:
Image classifier for Fashion-MNIST dataset featuring:

• CNN implementation
• Training/validation workflows
• Model evaluation metrics
• Sample prediction visualization

Key Files:

• data_loader.py - Image data handling
• cnn_model.py - Convolutional network architecture
• train.py - Training procedures
• evaluate.py - Accuracy/loss metrics

🚀 Getting Started

Prerequisites

pip install -r requirements.txt

Running Projects

# California Housing
cd California-Housing
python main.py

# Fashion MNIST
cd Fashion-MNIST
python train.py

🛠️ Technical Stack

• Frameworks: TensorFlow 2.x, Keras
• Data Processing: NumPy, Pandas
• Visualization: Matplotlib, Seaborn
• Model Evaluation: scikit-learn

📊 Key Metrics

🤝 Contribution

Contributions welcome! Please:

1. Fork the repository
2. Create your feature branch
3. Submit a pull request

📜 License

MIT License - See LICENSE for details