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