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Applications of GraphML like Predicting Protein Folding
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เนื้อหาจัดทำโดย Brian Carter เนื้อหาพอดแคสต์ทั้งหมด รวมถึงตอน กราฟิก และคำอธิบายพอดแคสต์ได้รับการอัปโหลดและจัดหาให้โดยตรงจาก Brian Carter หรือพันธมิตรแพลตฟอร์มพอดแคสต์ของพวกเขา หากคุณเชื่อว่ามีบุคคลอื่นใช้งานที่มีลิขสิทธิ์ของคุณโดยไม่ได้รับอนุญาต คุณสามารถปฏิบัติตามขั้นตอนที่แสดงไว้ที่นี่ https://th.player.fm/legal
Solving an impossible mystery... forget what you thought was possible!
This is a discussion of a video from Stanford's CS224W course which focuses on the many applications of graph machine learning, a field that utilizes graph data structures to solve complex problems. The speaker highlights different tasks and their associated applications, classifying them into four levels: node level, where the focus is on individual nodes; edge level, analyzing relationships between pairs of nodes; subgraph level, examining groups of nodes; and graph level, analyzing the entire graph structure. The lecture provides a detailed overview of various applications in diverse fields, including protein folding, recommender systems, drug discovery, traffic prediction, and physics-based simulations.
Watch the video: https://www.youtube.com/watch?v=aBHC6xzx9YI
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