Advancements in NLP and Holography, COCONut Dataset for Segmentation Models, and DL Model Deployment Techniques

Discover the latest research on extending NLP context windows, improving 3D holographic image quality with deep learning, the COCONut dataset for next-gen segmentation models, and model deployment and serving techniques in the world of AI. Join us as we explore these groundbreaking advancements and their implications for the future of natural language processing, holography, computer vision, and deep learning model deployment.

Sources:
https://www.marktechpost.com/2024/04/21/this-ai-paper-from-peking-university-and-microsoft-proposes-longembed-to-extend-nlp-context-windows/
https://www.koreaittimes.com/news/articleView.html?idxno=130793
https://www.marktechpost.com/2024/04/21/coconut-a-high-quality-large-scale-dataset-for-next-gen-segmentation-models/
https://medium.datadriveninvestor.com/dl-tutorial-38-model-deployment-and-serving-techniques-673c530266c2

Outline:
(00:00:00) Introduction
(00:00:42) This AI Paper from Peking University and Microsoft Proposes LongEmbed to Extend NLP Context Windows
(00:03:16) Improving 3D Holographic Image Quality with Deep Learning
(00:05:13) COCONut: A High-Quality, Large-Scale Dataset for Next-Gen Segmentation Models
(00:09:07) DL Tutorial 38 — Model Deployment and Serving Techniques