Networks: Computer Representation

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

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

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

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

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