Explore graph neural networks through comprehensive video tutorials covering both theoretical foundations and practical implementation. Master the application of machine learning to graph-structured data using deep graph encoders and state-of-the-art neural network architectures. Learn to implement node classification with Graph Convolutional Networks (GCN) using PyTorch and DGL, understand message passing mechanisms with detailed examples, and code GCNs from scratch using pure Keras. Discover knowledge graph embeddings for real-world applications including medical use cases, and explore topological approaches including simplicial complexes and persistent homology for advanced graph analysis. Get hands-on experience with major graph neural network libraries including PyTorch Geometric (PyG), Deep Graph Library (DGL), TensorFlow's graph neural network library, and DeepMind's JRAPH with JAX. Understand the mathematical foundations behind graph kernels, feature engineering for graphs, and representation learning for low-dimensional graph embeddings. Explore cutting-edge architectures from GraphSAGE to GraphBERT, learn about symmetry in graph neural networks, and implement neural Bellman-Ford networks for link prediction. Gain insights into the future of graph neural networks beyond traditional message passing, including topological layers and advanced geometric deep learning concepts.

Syllabus

Knowledge Graph Embedding - Dec 2021
PyTorch: Node Classification w/ Graph Neural Network on DGL for GCN
Discover the Future of Graph Neural Networks | Beyond message passing
Topological Message Passing on GNN | SIMPLICIAL COMPLEXES on CW Networks #ai
Message Passing for graphs - explained w/ example
Graph Neural Networks: GCN w/ pure KERAS coding
How to create a Graph for Graph Neural Networks?
First look at Knowledge Graph Embedding (w/ simple Jupyter NB dgl-ke)
Knowledge Graph for a Medical Application - DEMO in Python
NEW TOPOLOGICAL LAYER in Graph Neural Networks (GCN), Filtrations, Persistent Homology - ICLR 2022
PyG - PyTorch Geometric - Intro to Graph Neural Networks - Outlook SBERT w/ PyG
Graph Convolution Networks GCN - WHY?
Learn low-dim Embeddings that encode GRAPH structure (data) : "Representation Learning" /arXiv
Graph Neural Networks - what the Hell?
NEW TensorFlow Library on GRAPH Neural Networks released (Nov 2021)
What are Graph Kernels? Graph Kernels explained, Python + Graph Neural Networks
Alternative to PyG: Mighty DEEP GRAPH Library DGL (your black belt GraphAI)
Strange JRAPH - Deep Mind's GNN Library for Graph Neural Networks (w/ JAX)
First look at PyTorch Geometric: PyG 2.0 (Nov 2021)
My TOP 9 videos to understand & code GNN - Graph Neural Network
JAX explained in 1 Minute w/ Neural Network Code!
Google's AutoGrad + Tensorflow's XLA Linear Algebra Compiler = JAX
Your Brain will overload: Symmetry on Graph Neural Networks - R. Feynman to Bronstein
GraphSAGE to GraphBERT - Theory of Graph Neural Networks
What is Feature Engineering for Graphs? #Shorts
Learn Graph Neural Network + new videos on Neural Bellman-Ford + NodePiece
Lie Groups for Deep Learning w/ Graph Neural Networks
NEURAL Bellman-Ford NETWORK - 2022 Neural BFNet - Graph Neural Networks w/ Link Prediction AI
Unlocking the Potential of Message Passing: Exploring GraphSAGE, GCN and GAT | GNN GraphML