Generative AI

Course Outline

Module 1: Introduction to Generative AI

• Overview of Artificial Intelligence and Machine Learning
• Understanding Generative AI: Definitions and Applications
• History and Evolution of Generative AI
• Key Tools and Frameworks: TensorFlow, PyTorch, and Others

Module 2: Foundations of Generative Models

• Introduction to Neural Networks and Deep Learning
• Autoencoders and Variational Autoencoders (VAEs)
• Generative Adversarial Networks (GANs): Theory and Variants
• Transformers and Diffusion Models

Module 3: Practical Applications of Generative AI

• Text Generation: Language Models (e.g., GPT, LLaMA)
• Image Generation: Tools like Stable Diffusion and DALL·E
• Audio and Video Synthesis
• Ethical Considerations and Responsible AI

Module 4: Building Generative AI Solutions

• Data Preparation and Preprocessing
• Training and Fine-Tuning Generative Models
• Deploying Generative AI Applications
• Integration with Real-World Systems

Module 5: Advanced Topics and Future Trends

• Zero-Shot and Few-Shot Learning in Generative AI
• Multimodal Generative AI
• Customizing and Fine-Tuning Large Language Models
• Trends in Generative AI Research and Development

Course Objectives

1. Provide participants with a deep understanding of Generative AI concepts, technologies, and tools.
2. Equip learners with the skills to build, train, and deploy generative AI models.
3. Promote awareness of ethical considerations and responsible use of generative AI.
4. Encourage innovation in developing real-world applications using generative AI.

Learning Outcomes

By the end of the course, participants will be able to:

1. Explain the core concepts, methodologies, and frameworks behind generative AI.
2. Identify and implement different generative model architectures such as GANs, VAEs, and transformers.
3. Use generative AI tools to create applications for text, image, and audio generation.
4. Prepare datasets and fine-tune models for specific use cases.
5. Evaluate and mitigate ethical risks in generative AI applications.
6. Apply generative AI knowledge to solve real-world problems innovatively.

Methodology

1. Interactive Lectures: Foundational knowledge delivered through instructor-led sessions.
2. Hands-On Labs: Practical sessions to implement generative models and explore AI tools.
3. Case Studies: Real-world scenarios and use cases to highlight applications of generative AI.
4. Group Projects: Collaborative projects to design and deploy generative AI applications.
5. Assessment & Feedback: Periodic quizzes, assignments, and peer/instructor reviews.
6. Capstone Project: End-of-course project where participants create and showcase a generative AI solution.
7. Guest Speakers: Industry experts to share insights into emerging trends and real-world challenges.