Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
Deep dive into Graph Convolutional Networks - spectral graph theory, message passing, aggregation mechanisms, and applications in node classification and graph 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