Deep Learning and Comparisons between Large Language Models

Abstract: 

Deep learning especially large language models has been gaining a lot of recent traction from research community. This talk builds some background in deep learning towards explaining the concepts of large language models. Afterward, this talk lists different popular large language models, conducts brief comparison in terms of techniques and accuracy results among different large language models.

Bio: 

Hossam Amer joined Microsoft as a scientist in 2021. His research interests are Image/Video Compression, Computer Vision, and most recently Natural Language Processing. Hossam is contributing to many products including Microsoft Translator and Microsoft SwiftKey. Prior to joining Microsoft, Hossam was a Postdoctoral-Fellow at the Multimedia Communications Lab at the University of Waterloo (UW), where he mentored several MSc and PhD students. He obtained his PhD from the same lab, where he received the prestigious annual UW teaching award based on students' and instructors' nominations as well as published papers in top venues. Hossam also acts as a reviewer in several IEEE conferences and journals and supervises students in research and teaching. In addition, Hossam was the Chair of the ECE Graduate Student Association at UW. Hossam is a strong believer in constantly transferring his knowledge in order to make a difference.

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