Scroll below for tutorials and datasets.

## G-Invariant & G-Equivariant Neural Networks:

A complete beginner's guide with step-by-step examples

In this step-by-step tutorial we introduce the mathematical foundations of the most common type of group-invariant and group-equivariant representation learning. Examples include learning invariant representations of **graphs** and **images**.

## Sets and Point Cloud Representation Learning:

A complete beginner's guide with step-by-step examples

In this step-by-step tutorial we introduce the mathematical foundations of the most common type of set representations, including representations of point clouds through Lie G-invariant & G-equivariant representations.

Go to tutorial »## Graph Representation Learning:

A complete beginner's guide with step-by-step examples

In this step-by-step tutorial we introduce the mathematical foundations of graph and knowledge graph representations, including the connection between graph neural networks (GNNs) and eigenvectors.

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