Lectures related to Graph Neural Networks: Interconnected World

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Covers deep learning concepts, focusing on graphs, transformers, and their applications in multimodal data processing.

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

Introduces the basics of graph algorithms, covering traversal, representation, and data structures for BFS and DFS.

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Covers the fundamentals of connectivity in graph theory, including paths, cycles, and spanning trees.

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

Covers graph processing with a focus on Oracle Labs PGX, discussing graph analytics, databases, algorithms, and distributed analytics challenges.

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Explores the diagonalization of matrices and the conditions for exact diagonalization, with examples demonstrating the process.

Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

Covers the fundamentals of deep learning, from artificial neurons to modern networks.

Explores the ubiquity of graphs in modern data and analytics, focusing on the shift in organizations' perception of graph technologies.