Thinking outside of the Euclidean Space: An introduction study to Graph Machine Learning and its Applications
Algorithms
• November 2021
Thinking outside of the Euclidean Space: An introduction study to Graph Machine Learning and its Applications
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Thinking outside of the Euclidean Space: An introduction study to Graph Machine Learning and its Applications
About
Convolutional Neural Networks is a well known method to handle euclidean data structures like image pixels, texts, time series, or even point clouds. However in real world, we are also surrounded by the non-euclidean data structure like graphs (e.g. social networks) and a machine learning method to handle this type of data is known as Graph Neural Networks.
Therefore, in this 120 minutes workshop we will deep dive into an introduction study of Graph Machine Learning and its Applications in various domains followed by an interactive Hands-On-Session with a real world case study.
Language
English
Level
Intermediate
Length
112 minutes
Type
online conference
About the speaker
About the speaker
Sachin Sharma
Machine Learning Research Engineer •
ArangoDB
Sachin is a Machine Learning Research Engineer at ArangoDB whose aim is to build Intelligent products using thorough research and engineering in the area of Graph Machine Learning. He completed his Masters’s degree in Computer Science with a specialisation in Intelligent Systems. He is an AI Enthusiast who has conducted research in the areas of Computer Vision, NLP, and Graph Neural Networks at DFKI (German Research Centre for AI) during his academic career. Sachin also worked on building Machine Learning pipelines at Define Media Gmbh where he worked as a Machine Learning Scientist.
Details
Language
English
Level
Intermediate
Length
112 minutes
Type
online conference
Belongs to:
Belongs to:
Online Tech Conference 2021 - English Edition | Autumn
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