Practical Geometric Deep Learning in Python • Pantelis Elinas • YOW! 2019

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This presentation was recorded at YOW! 2019. #GOTOcon #YOW https://yowcon.com Pantelis Elinas - Principal AI/ML Engineer & Team Leader at CSIRO's DATA61 RESOURCES https://www.linkedin.com/in/pantelis-elinas-370971119 https://medium.com/@pantelis.elinas https://github.com/PantelisElinas ABSTRACT #GeometricDeepLearning (#GDL) is a fast developing machine learning specialisation that uses the network structure underlying the data to improve learning outcomes. GDL has been successfully applied to problems in various domains with network-structured data, such as social science, medicine, media, finance, etc. Inspired by the success of neural networks in domains such as computer vision and natural language processing, the core component driving GDL is the #GraphConvolutionOperator. This operator is used as the building block for deep learning models applied to networks. This approach takes advantage of many algorithmic and computational developments from modern neural network research and practice – such as composability, optimisation, and end-to-end training – to improve predictive performance. However, there is a lack of tools for geometric #DeepLearning targeting data scientists and #MachineLearning practitioners. In response, #CSIRO’s #Data61 has developed #StellarGraph, an open source #Python library. StellarGraph implements a number of state-of-the-art methods for GDL with a clean and consistent API. Furthermore, StellarGraph is designed to make the application of GDL a

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