Generative AI and Large Language Models

Overview

Welcome to the world of Generative AI and Large Language Models (LLMs)—where technology mirrors human creativity and intelligence. This course is designed to provide you with a comprehensive understanding of generative models, including their evolution, applications, and the underlying architectures that make them possible. Throughout the modules, you'll explore various generative techniques such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), diffusion models, and multimodal AI. You'll also gain hands-on experience with tools like OpenAI's GPT, Hugging Face, Streamlit, and MLflow, ensuring you can deploy and fine-tune models for real-world applications.

Syllabus

Introduction to Generative AI

Take your first steps into the exciting world of generative AI, where you'll distinguish between various model types including GANs, VAEs, transformers, and diffusion models. You'll explore the evolution of generative technologies and examine their real-world applications while considering important ethical implications that accompany these powerful tools.

Large Language Models (LLMs) & Transformer Architecture

Explore the revolutionary transformer architecture that powers today's most advanced language models. You'll gain hands-on experience with self-attention mechanisms, learn how transformers process and generate text, and experiment with fine-tuning using Hugging Face Transformers. This module bridges theory with practical implementation, equipping you with skills to work directly with cutting-edge LLM technology.

Hands-on Applications of LLMs

Take your LLM knowledge to the next level with practical applications that power modern AI systems. You'll implement retrieval-augmented generation to enhance responses with external knowledge, use structured output techniques for consistent formatting, and deploy models through APIs. This module tackles both the theory and practice behind modern LLM applications, showing you how to build real-world applications with today's most advanced language models.

Diffusion Models

Discover the technology behind today's most impressive image generation systems. You'll learn how diffusion models gradually transform random noise into stunning visuals through an iterative denoising process. Through practical coding exercises, you'll implement your own diffusion model using PyTorch, explore Stable Diffusion for text-to-image generation, and compare diffusion with earlier approaches like GANs and VAEs to understand why diffusion has become the dominant paradigm in visual generation.

Multimodal Generative AI

Discover how cutting-edge AI models can integrate text, images, and audio to create truly multimodal experiences. You'll investigate vision-language models like CLIP and BLIP that understand relationships between text and images, implement audio-based AI with Whisper for speech recognition, and gain hands-on experience building systems that can process multiple types of data simultaneously. This module prepares you for the increasingly multimodal future of generative AI where models seamlessly combine different kinds of information.