Networks: Computer Representation

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Related lectures (29)

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Graphical Models: Representing Probabilistic Distributions

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

Graphs: BFS

Introduces elementary graph algorithms, focusing on Breadth-First Search and Depth-First Search.

Graph Algorithms: Modeling and Representation

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

Graph Algorithms: Memory Management and Traversal

Explores memory management, graph representation, and traversal algorithms in Python, emphasizing BFS and DFS.

Graphs in Deep Learning: Applications and Techniques

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

Networked Control Systems: Laplacian Matrix and Consensus

Explores the Laplacian matrix and consensus in networked control systems.

Graph Algorithms: Basics

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

Networked Control Systems: Properties and Connectivity

Explores properties of matrices, irreducibility, and graph connectivity in networked control systems.

Depth-First Search: Traversing and Sorting Graphs

Explores depth-first search, breadth-first search, graph representation, and topological sorting in graphs.

Irreducible Matrices and Strong Connectivity

Explores irreducible matrices and strong connectivity in networked control systems, emphasizing the importance of adjacency matrices and graph structures.