Generative AI with LLMs

About

Structure

Day 1: Introduction to Generative AI

Generative vs. Discriminative Models

Overview of Generative AI

Applications of generative AI

Day 2: Basics of Language Models

Understanding Language Models

Unigram, Bigram, N-gram Models

Neural network based (seq2seq) Language Models

Day 3: Transformer Models and Attention Mechanism

Introduction to Transformer Models

Self-Attention and Multi-Head Attention

BERT

Day 4: Large Language Models (LLMs)

Introduction to LLMs

Architecture of GPT-4

Fine-tuning LLMs

Day 5: Evaluating and Improving LLMs

Evaluation metrics

Issues in LLMs

Prompt engineering

Course leader

Kunpeng Zhang from Smith School of Business, University of Maryland, United States

Target group

The GSERM Global School in Empirical Research Methods is a high-calibre programme dedicated to teaching cutting-edge research methodology. We not only welcome PhD students, post-docs, and graduate students from all kinds of fields, but also professionals working outside of academia.

Prerequisites (knowledge of topic)
Basic knowledge of Python programming and familiarity with Machine Learning

Course aim

On successful completion of this course, students will be able to:

– Understand the fundamentals of Generative AI and Large Language Models.
– Understand how to Implement and fine-tune LLMs for various tasks.
– Evaluate the performance of LLMs using various metrics.
– Understand the ethical and societal implications of using LLMs.
– Develop their own projects utilizing Generative AI

Fee info