Generative Ai For Beginners

🚀 Generative AI for Beginners

This project offers a comprehensive introduction to generative AI, guiding you through installation, configuration, and learning with practical examples.

🚀 Quick Start

To start this course, follow these steps:

📦 Installation

1. Install Python: Ensure you have the latest version of Python installed on your system (Python 3.8 or higher is recommended).
2. Install pip: This is Python's package manager, usually installed with Python. If not, refer to the official documentation for installation.
3. Install dependencies:

   pip install azure-openai openai python-dotenv
   

🔑 Configure API Keys

1. Get an Azure OpenAI key: Visit the Azure OpenAI portal, create or select an existing resource group, and follow the guide to create a new OpenAI service instance. After creation, navigate to the "Keys" section to obtain the access key.
2. Set environment variables:
   • Create a file named .env (if it doesn't exist).
   • Add the following content, replacing your_api_key with your actual API key:

     AZURE_OPENAI_API_KEY=your_api_key
     

   • Save the file.

📂 Clone the Repository

1. Clone the GitHub repository:

   git clone https://github.com/microsoft/generative-ai-for-beginners.git
   cd generative-ai-for-beginners
   

▶️ Start the Project

1. Run the example code:
   • Open a terminal and navigate to the project's root directory.
   • Run the following command to start the first example:

     python3 src/introduction_to_genai.py
     

2. Explore other courses: Visit the lessons folder to view and run code examples for different courses.

📚 Learning Resources

📖 Conceptual Courses (6 Lessons)

1. Introduction to Generative AI and LLM: Understand the basic concepts, working principles, and applications of generative AI in various fields.
2. Text Generation Models: Delve into how to train and use text generation models, including model architectures and optimization techniques.
3. Image Generation Techniques: Learn the latest image generation methods, such as diffusion models and adversarial networks.
4. Audio Synthesis and Speech Generation: Explore techniques for converting text to speech and methods for generating high - quality audio.
5. Multimodal AI Applications: Understand how to combine different data types (e.g., text, images, audio) to improve model performance.
6. Ethics and Security: Discuss the ethical issues, security challenges, and coping strategies of generative AI.

💻 Coding Courses (6 Lessons)

1. Beginner Projects: Start with simple text generation tasks to familiarize yourself with API calls and model interactions.
2. Advanced Text Generation: Learn how to optimize generation results, handle complex queries, and implement dialogue systems.
3. Image and Vision Applications: Develop a basic image generator and explore its applications in computer vision.
4. Audio and Speech Synthesis: Create a text - to - speech conversion tool and understand the latest speech synthesis techniques.
5. Multimodal Project Integration: Integrate data from different modalities to build a comprehensive AI system.
6. Security and Ethical Practices: Implement content filtering mechanisms to ensure that the output of generative AI complies with ethical standards.

👥 Interact with Other Learners

Join our official Discord server and participate in the "#generative-ai-beginners" channel in the discussion area. Meet other learners and share your experiences. The project team members will also provide support and guidance in this channel.

🤝 Contribution Guidelines

📋 How to Contribute

1. Report issues: If you find any errors or have improvement suggestions, submit a GitHub issue.
2. Submit a pull request: After fixing issues, adding new features, or improving the documentation, you can submit a Pull Request.
3. Translate content: If you are proficient in a certain language, please help translate the course content into that language. Ensure the translation quality and avoid using machine translation.

📃 Contribution Guidelines

1. Read the CLA: Before submitting contributions, carefully read and agree to the Microsoft Open Source Contributor Agreement.
2. Follow the code style: Keep the code and documentation in a consistent format. Refer to the existing code style for modifications.

🚀 Start Learning

Now that you have completed all the necessary preparations, let's start with the Introduction to Generative AI and LLM!