Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
How Flash Attention, Multi-Head Attention (MHA), Grouped-Query Attention (GQA), and Multi-Query Attention (MQA) compare — algorithm vs architecture, KV-cache memory, quality trade-offs, and how to choose for production transformer inference.
Learn about attention sinks, where LLMs concentrate attention on initial tokens, and how preserving them enables streaming inference.
Understand cross-attention, the mechanism that enables transformers to align and fuse information from different sources, sequences, or modalities.
Learn how Grouped-Query Attention (GQA) balances Multi-Head quality with Multi-Query efficiency for faster LLM inference.
Explore linear complexity attention mechanisms including Performer, Linformer, and other efficient transformers that scale to very long sequences.
Learn how masked attention enables autoregressive generation and prevents information leakage in transformers and language models.