Course Overview


This course was designed to get you up to speed with Graph Neural Networks so that you can both understand seminal papers in the field and implement GNNs using modern software tools. It was constructed around three elements:


  • Theory, to familiarize you with the foundational topics quickly and efficiently (i.e., not more complicated than it needs to be)
  • Practice, to walk you thru Hands-On examples that span the major applications of Node Classification, Link Prediction and Graph Classification and discuss practical engineering problems. This section is limited to a cohort of 25 students.
  • Community, to get you engaged with fellow students in the form of group discussion and code reviews. Since this works best with a cohort of students, it will be wrapped into the "Hands-On" section and also limited to 25 students.


The Theory section consists of self-paced videos, quizzes and a curated set of references designed to efficiently teach you the core mathematical concepts that underpin the foundations of GNNs. This section is self-directed and fully available at the time of enrollment.

The Hands-on section is a synchronous experience limited in capacity to 25 participants over a 6-week period in which a cohort of students work on the same material and help one another through e.g., code reviews. The content consists of model-building code exercises, explanatory videos that walk thru the answer keys, and an additional set of lecture-style videos that frame the ML tasks and discuss practical problems of building GNNs, like scalability and over-smoothing. See the FAQ below for the Hands-On course schedule.

It's expected that students of this course participate in community activities, such as discussions in chat and peer-to-peer code reviews. All participants will receive access to a course GitHub organization where course and student code will be hosted, and a private Discord room where Zak and students will discuss course material and engage with the community.

This course assumes basic familiarity with GitHub and building ML models in PyTorch. Please see the component course pages for additional details.

Full Course Contents


Bundle the two courses to get a discounted rate.

Click thru to the individual course pages for preview content.

Foundational Theory of Graph Neural Networks

A self-paced course that covers the fundamental mathematical theory underlying GNNs

Zak Jost

Zak Jost

$39

This bundle is not available for purchase.

Join the Waitlist

Enrollment for Hands-On is currently CLOSED. Please join the Waitlist below for announcements on future cohorts, and get started in the Foundational Theory of GNNs course now.

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Students of the Theory section will receive first priority and get the discounted bundled rate when enrollment for Hands-On opens.

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    Frequently Asked Questions


    What is the schedule for the Hands-on course?

    Enrollment will be open until the maximum number of students is met or until the start date, whichever occurs first. The course material will begin being released on the start date and the 3 sub-sections will last 2 weeks each. The course will complete within six weeks. See the Course Curriculum section for more details.


    Can I get a refund if I'm unhappy with my purchase?

    All purchases have a 30-day money back guarantee.

    GNNs as the "Next Big Thing"


    The topic of Graph Representation Learning has been exploding in popularity, but it's still relatively early days. Between 10-20% of all papers published at top conferences were on the topic of ML on graphs. Despite this popularity in the research community, these methods are just beginning to gain traction in industry as the toolsets mature (e.g., DeepMind with Travel time estimation in Google Maps). For those looking to be at the bleeding edge, this is a wonderful time to jump in.

    Hi, I'm Zak


    I am an Applied Scientist in FAANG that specializes in building systems supporting GNNs in industry. I also run the WelcomeAIOverlords YouTube channel, a Discord community and blog. This course uses the same approach of explaining things simply, but will go much deeper into the theory and applications of GNNs.