What does graph have to do with machine learning? A lot, actually. Machine learning, and its deep learning subdomain, make a great match for graphs. Machine learning on graphs is an emerging technology full of promise.
Amazon, Alibaba and Apple are just some of the organizations using this in production, and advancing the state of the art. Already, more than 20% of the research published in top AI conferences is graph-related.
We have kept an eye on this extraordinarily promising approach, and its interplay with knowledge graphs and semantic technologies. We have seen the progress, and the traction.
“Domain knowledge can effectively help a deep learning system bootstrap its knowledge, by encoding primitives instead of forcing the model to learn these from scratch”.
Our first event for 2021 is focused on graph-based data science and machine learning.
Join us to learn from top minds in the domain, spread the knowledge, exchange experience, network, socialize, and get hands on!
How do you go from zero to hero in Graph Machine Learning in 2 months or less? Microsoft Software – Deep Learning Engineer and AI Epiphany Founder Aleksa Gordic did just that, and he shares his resources and experience. Everything he wishes his younger self knew when he set out to implement his open source GAT project: PyTorch-based Graph Attention network
How do you integrate Python’s excellent libraries for working with graphs, and leverage disparate techniques in ways that complement each other? Derwen Founder, data science player – coach Paco Nathan introduces kglab, an open source project that provides ways to produce Hybrid AI solutions for industry use cases.
If that whets your appetite, and you wish you had someone to guide you through the process of getting deep down and technical with the code..you’re in luck. Paco will help you do just that, in a dedicated 2-hour long hands-on session! If you were at his Knowledge Connexions Masterclass in December, you know. If not, here’s a hint: seats are limited – don’t think twice.
How about you? Are you using graph-based data science and machine learning? Do you have a use case, a technical breakthrough, an interesting story to share, or some hands-on skills to teach? We want to hear from you! We are open to your submissions until the 15th of March. Would you like to share your knowledge with the world, and share the stage with our speakers? Don’t be shy!