The workshop is a slightly advanced practical introduction to graph neural networks in Python. We’ll start with a theoretical introduction, then we’ll move on to practical part. The practical part will consist of 3 main sub-parts:
• Preparing your own datasets
• Model implementations using Spektral and functional TensorFlow / Keras API
• Model implementations using Spektral and TensorFlow / Keras model sub-classing
Brief Bullet Point Outline:
• Introduction & theory (15 min)
• Spektral: layers, datasets and data loaders (15 min)
• Practice: Node-level classificatrion (20 min)
• Practice: Graph-level classification (20 min)
• Building your own dataset (10 min)
• Q&A (10 min)
People of all backgrounds and experience levels are welcome to the workshop. However, to get the most out of the workshop, the following skills are recommended:
• Basic understanding of graph structures
• Good understanding of basic machine learning & deep learning concepts
• Good understanding of Python
• Good understanding of Keras workflows (incl. functional API and model sub-classing)
• Basic understanding of TensorFlow 2.x
Currently engaged in a non-profit research initiative at Tensorcell, where I work with graph neural networks. For the last 3.5 years, I worked as an Innovation Lead and Machine Learning Researcher at Data Science and Artificial Intelligence Center of Excellence at Lingaro, building end-to-end machine learning systems for our global customers.
I am also a speaker and a blogger, author of #SundayAiPapers - a weekly LinkedIn microblog presenting the most recent papers on natural language processing, causal inference and probabilistic modeling and Medium blogs author. I am interested in NLP, causality, probabilistic modeling and graph neural networks.
I love traveling with my wife. I am passionate about vegan food, languages and running.