• What is Generative AI? History & Key Applications
• Types of Generative Models (GANs, VAEs, Transformers, Diffusion)
• Recap of Attention Mechanisms
• The Transformer Architecture (Self-Attention, Positional Encoding, Feed-Forward)
• Encoder-Decoder Transformers
• Introduction to Causal Attention (for text generation)
• Setting up Environment (Hugging Face Transformers, PyTorch/TensorFlow)

• LLM Architectures (GPT, Llama, etc. concepts)
• Tokenization Strategies
• Working with Pre-trained LLMs
• Text Generation Techniques (Sampling, Beam Search, Top-k, Nucleus Sampling)
• Prompt Engineering Fundamentals
• Few-shot and Zero-shot Learning with LLMs
• Implementing Text Generation Tasks (Creative Writing, Summarization)

• Introduction to Image Data & Generative Image Tasks
• Generative Adversarial Networks (GANs): Generator & Discriminator
• Training GANs: The Minimax Game
• Variational Autoencoders (VAEs) Basics
• Introduction to Diffusion Models (Forward & Reverse Process)
• Implementing Simple GANs or VAEs
• Working with Pre-trained Diffusion Models (Stable Diffusion/DALL-E concepts)
• Text-to-Image Synthesis Techniques

• Loss Functions specific to GenAI (Generator/Discriminator Loss, Reconstruction Loss, Diffusion Loss)
• Optimizers for Generative Models
• Training Challenges (Mode Collapse, Training Stability)
• Evaluating Generative Models (FID, IS, Perplexity - concepts)
• Introduction to Fine-tuning Pre-trained Models
• Parameter-Efficient Fine-tuning (PEFT): LoRA, QLoRA concepts
• Hands-on Fine-tuning Example (Text or Image Model)

• Reinforcement Learning from Human Feedback (RLHF) - Conceptual
• Multi-modal Generative AI (Text-Image, Image-Text concepts)
• Introduction to other GenAI types (Music, Video, Code Generation - concepts)
• Bias and Fairness in Generative Models
• Ethical Use and Misuse of Generative AI
• Copyright and Ownership of Generated Content
• Responsible Deployment Practices

• Saving and Loading Generative Models
• Introduction to Model Deployment Considerations
• Building a Simple REST API for Model Inference (using Flask - Basic)
• Containerization Concepts (Docker - Optional)
• Generative AI Capstone Project: End-to-End Implementation on a Creative Task
• Presenting Your Project
• Career Paths in Generative AI & Portfolio Building

This course is ideal for developers, data scientists, and AI enthusiasts with a solid background in Python programming, machine learning fundamentals, and preferably some exposure to deep learning. It's for those looking to specialize in creating AI models that generate content and who want to understand the underlying architectures like Transformers, GANs, and Diffusion models.

Strong Python programming skills, including libraries like NumPy and Pandas, are necessary. Prior knowledge of machine learning concepts and practical experience with deep learning frameworks like TensorFlow or PyTorch is highly recommended. A foundational understanding of linear algebra and calculus is also beneficial.

You will learn about Transformer networks (the basis for LLMs like GPT), Generative Adversarial Networks (GANs), and Diffusion Models. The course covers text generation techniques (like prompting and sampling), image synthesis, and methods for fine-tuning models using libraries like Hugging Face Transformers.

Yes, ethical considerations are a crucial part of the course. We will discuss topics such as bias in generative models, responsible deployment, potential misuse, copyright issues related to generated content, and fairness.

Generative AI is a specific field within Artificial Intelligence and often relies heavily on Deep Learning techniques and architectures. Models like Transformers, GANs, and Diffusion Models are all complex neural networks (deep learning models) specifically designed for generating new data. A strong foundation in deep learning is beneficial for understanding the technical details covered in this course.