Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
Understanding node importance through centrality measures, shortest paths, hop distances, clustering coefficients, and fundamental graph metrics
Learn Graph Convolutional Networks (GCN) with spectral theory, message passing, and node classification for geometric deep learning.
Learning low-dimensional vector representations of graphs through random walks, DeepWalk, Node2Vec, and skip-gram models
Hierarchical graph coarsening techniques - TopK, SAGPool, DiffPool, and readout operations for graph-level representations
Understanding sparse mixture of experts models - architecture, routing mechanisms, load balancing, and efficient scaling strategies for large language models
Deep dive into the fundamental processing unit of modern GPUs - the Streaming Multiprocessor architecture, execution model, and memory hierarchy