ADLRG Mini-Course: Generative AI (Vision) 2
OnlineTitle: Generative AI (Vision) 2Date: Nov 13, 2023Time: 4-6pm CSTLocation: Online https://forms.gle/o8t4NAnEcfBE1GJ6A
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Prompt Engineering and Retrieval Augmented Generation
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 […]
TrAC Seminar Series – John Evans
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 […]
DLRG – Fundamentals of Deep Learning – 1
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 over 3 weeks.
$20
ADLRG – Deep Reinforcement Learning – 1
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/
DLRG – Fundamentals of Deep Learning – 2
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.
TrAC Seminar Series – Hari Subramoni
Title: HARVEST: High-Performance Artificial Vision Framework for Expert Labeling using Semi-Supervised Training Abstract Three valuable crop scouting use cases are to provide i) recommendations of optimum rate and timing of […]
ADLRG – Deep Reinforcement Learning – 2
TrAC ADLRG Mini-course on Deep Reinforcement Learning (class 2/3) In this mini-course, we will cover concepts in deep reinforcement learning.
Journal Club – 101 Topics in ML, Control & Optimization
Introduction Optimization has been playing a critical role in modern machine learning, leading to its significantly successful applications in many different fields. Researchers and practitioners typically spend less time investigating optimizers when they do the model training, mostly following some empirical rules or recommendations from the community. In this event, we will go relatively deeper […]