Educational MVPs for Learning and Development

This repository hosts a collection of open-source Minimum Viable Products (MVPs) designed and developed for educational and learning purposes. These MVPs aim to provide practical, hands-on examples for developers, students, and educators to learn various programming concepts, AI/ML techniques, and software development practices.

Purpose

The goal of this repository is to:

• Share educational and development-focused MVPs.
• Enable learning through real-world examples and implementations.
• Encourage contributions, experimentation, and exploration.

Current MVPs

1. MNIST Handwritten Digit Recognition

• Description: A TensorFlow-based AI model that recognizes handwritten digits using the MNIST dataset.
• Features: Dataset loading, model training, evaluation, and image prediction.
• Learn More: Refer to the README.md in the respective directory for detailed instructions.

How to Use

1. Clone the repository:

   git clone https://github.com/salamalsam/AI-MVPs.git

2. Navigate to the directory of the MVP you want to explore.
3. Follow the instructions in the specific README.md file of that MVP.

Prerequisites

• Python 3.7 or later
• Virtual environment setup (recommended)
• Project-specific dependencies listed in each MVP's requirements.txt

Setting Up the Environment

For consistency across MVPs, use the following steps to set up your Python environment:

1. Create a virtual environment:

   python -m venv env

2. Activate the virtual environment:
   • Windows:

     env\Scripts\activate

   • macOS/Linux:

     source env/bin/activate

3. Install dependencies from the requirements.txt file of the specific MVP:

   pip install -r requirements.txt

Contribution

Contributions are welcome! If you have ideas for new MVPs or improvements to existing ones, feel free to:

• Fork the repository
• Create a branch for your changes
• Submit a pull request

Disclaimer

While these MVPs are designed with educational intent, they may not be fully optimized for production use. AI-related projects, in particular, may have limitations or inaccuracies and should be verified carefully when used in practical scenarios.

License

This repository is open-source and licensed under the MIT License. Feel free to use, modify, and distribute the code with attribution.

Future Plans

More MVPs will be added over time, focusing on:

• Machine Learning and AI
• Web Development
• Data Science
• Internet of Things (IoT)
• Educational Games

Stay tuned for updates!

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