Overview

This course is designed to provide an in-depth understanding of generative artificial intelligence with a focus on Large Language Models (LLMs) and Diffusion models. The course covers the foundations of LLMs, including their architecture, training, and fine-tuning, and explores their use in natural language processing tasks such as text generation, summarization, and translation.

Topics Covered

• Introduction to Generative AI
• Foundations of LLMs
• Transformer Architecture
• Training LLMs
• Fine-tuning LLMs for specific tasks
• Natural language processing tasks with LLMs (text generation, summarization, translation, etc.)
• Applications of LLMs
• Challenges and limitations of generative AI

Learning Objectives

• Understand the foundations of generative AI, including LLMs
• Understand the transformer architecture and how it enables LLMs to generate human-like language
• Understand the training process for LLMs
• Understand how to fine-tune LLMs
• Understand the challenges and limitations of generative AI

Prerequisites

• Basic programming knowledge (Python)
• Basic knowledge of machine learning fundamentals (neural networks, training, etc.)
• Familiarity with natural language processing concepts

Course Format

The course will consist of two lectures and hands-on coding exercises. The lectures will cover the theoretical foundations of LLMs, and the hands-on coding exercises will provide practical experience with prompt engineering, training and fine-tuning these models.

Assessment

MCQ test