ADLRG Mini-Course: Scientific Machine Learning 3
OnlineTitle: Scientific Machine Learning 3Date: Oct 30, 2023Time: 4-6pm CSTLocation: Online https://forms.gle/o8t4NAnEcfBE1GJ6A
Title: Scientific Machine Learning 3Date: Oct 30, 2023Time: 4-6pm CSTLocation: Online https://forms.gle/o8t4NAnEcfBE1GJ6A
Title: Building Transformers Models for Conversational AI ToolsDate: Nov 6, 2023Time: 1-3:30 pm CSTLocation: Online Registration: https://forms.gle/o8t4NAnEcfBE1GJ6A Learning Objectives Learn how to quickly build and deploy production-quality speech AI applications […]
Title: Generative AI (Vision) 1Date: Nov 6, 2023Time: 4-6pm CSTLocation: Online https://forms.gle/o8t4NAnEcfBE1GJ6A
Title: Building Transformers Models for Conversational AI ToolsDate: Nov 13, 2023Time: 1-3:30 pm CSTLocation: Online https://forms.gle/o8t4NAnEcfBE1GJ6A Learning Objectives Learn how to quickly build and deploy production-quality speech AI applications with […]
Title: Generative AI (Vision) 2Date: Nov 13, 2023Time: 4-6pm CSTLocation: Online https://forms.gle/o8t4NAnEcfBE1GJ6A
This intensive 8-hour tutorial will provide a hands-on introduction to two of the most important techniques for getting the most out of large language models: prompt engineering and retrieval augmented […]
Title: Data-Driven Turbulence Modeling and Simulation Abstract Turbulent fluid flows are characterized by a wide spectrum of spatial and temporal scales.Unfortunately, the cost of resolving these scales with Direct Numerical […]
TrAC DLRG Mini-course on Fundamentals of Deep Learning (class 1/3) In this mini-course, we will cover basic fundamental concepts of deep learning. This is a series of 3 lectures spread […]
TrAC ADLRG Mini-course on Deep Reinforcement Learning (class 1/3) In this mini-course, we will cover concepts in deep reinforcement learning. To register for this course, visit this link: https://trac-ai.iastate.edu/2024/01/16/trac-events-for-spring-2024/news/
TrAC DLRG Mini-course on Fundamentals of Deep Learning (class 2/3) In this mini-course, we will cover basic fundamental concepts of deep learning. This is a series of 3 lectures spread over 3 weeks.