Course Outline

Day 1: Foundations of Generative AI 

Session 1: Introduction to Generative AI 

• What is Generative AI?
• History and evolution of generative models 
• Applications across industries (art, finance, healthcare, gaming, etc.) 
• Ethical considerations and challenges 

Session 2: Understanding Core Generative Models 

• Generative Adversarial Networks (GANs) 
• Variational Autoencoders (VAEs) 
• Transformer-based models (GPT, BERT, T5, etc.) 
• Diffusion models (Stable Diffusion, DALL·E) 

Session 3: Building Your First Generative Model 

• Setting up Python environment (TensorFlow/PyTorch)
• Hands-on: Implementing a basic VAE or GAN
• Fine-tuning parameters and evaluating generated outputs 

Session 4: Generative AI for Text Generation 

• Language models and their architectures (GPT-3, GPT-4, LLaMA)
• Prompt engineering techniques 
• Hands-on: Generating text with OpenAI’s API or Hugging Face models 

Day 2: Advanced Techniques & Applications 

Session 5: Image and Video Generation

• Diffusion models and their role in AI art generation 
• Training GANs for image synthesis 
• Hands-on: Generating images with Stable Diffusion 

Session 6: Audio and Music Generation 

• AI-generated music (Jukebox, Magenta) 
• AI voice synthesis and text-to-speech (TTS) 
• Hands-on: Creating AI-generated voice or music samples

Session 7: Multimodal Generative AI (Text-to-Image, Image-to-Text, etc.) 

• Understanding CLIP and DALL·E 
• Hands-on: Generating text-based images and vice versa 

Day 3: Real-World Projects and Deployment 

Session 8: AI-Powered Chatbots and Virtual Assistants 

• Building AI-driven chatbots using generative models 
• Integrating AI-generated responses into applications 
• Hands-on: Creating an AI chatbot 

Session 9: Generative AI for Business and Industry 

• Use cases in marketing, healthcare, and finance
• Case studies: AI in product design, data augmentation, automation 
• Hands-on: AI-generated content for business applications 

Session 10: Future Trends & Ethical AI 

• The future of generative AI 
• Ethical AI, bias, and responsible AI development 
• Discussion and final project presentations