Lecture
This lecture covers the application of graphs in deep learning, focusing on their structure and the relationships between nodes and edges. The instructor begins by reviewing the previous week's content on natural language processing and transformers, emphasizing the importance of attention mechanisms. The lecture then transitions to graphs, explaining their composition of nodes and edges, and how these elements define the graph's structure. Key concepts such as adjacency matrices, node and edge embeddings, and various graph types are introduced. The instructor discusses tasks that can be performed on graphs, including classification and regression, and highlights the significance of graph convolutional networks. The lecture also addresses the challenges of oversmoothing in deep learning models applied to graphs and explores different aggregation methods for node information. Finally, the instructor outlines the upcoming mini-project, encouraging students to apply their knowledge of graph-based techniques to real-world problems.