Welcome to the Machine Learning and Deep Learning Open Lab Series! During this series, Dr. Namig Abbasov offers seven open labs to introduce participants to core concepts and techniques in Machine Learning (ML) and Deep Learning. These open labs will prioritize intuitive understanding of machine learning algorithms and deep learning approaches. These are intended to complement machine learning and deep learning courses taught at ASU by focusing on intuitive explanations of difficult concepts and examples with analogical illustrations. Below, you will find descriptions for each open lab of the series. Each open lab session is one hour long, allowing us to explore these topics as thoroughly as time permits.

Transformer-Based Models, the final lab of the Machine Learning and Deep Learning Series focuses on new developments in artificial intelligence, specifically transformer-based models. Participants will learn fundamental components of transformers and how they differ from traditional neural networks. The session will cover pretrained models like BERT, fine-tuning, and transfer learning, providing practical insights into their application. Participants will also explore the role of Large Language Models (LLMs) in AI advancements, the resurgence of RNNs, and discuss the future of AI. This lab equips attendees with the knowledge to understand and work with state-of-the-art models shaping the AI landscape.