Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
Interactively explore how self-attention allows Vision Transformers (ViT) to understand images by capturing global context. Click, explore, and see how it differs from CNNs.
Deep dive into Transparent Huge Pages (THP), a Linux kernel feature that automatically promotes 4KB pages to 2MB huge pages. Learn how THP reduces TLB misses, page table overhead, and improves performance—plus the hidden costs of memory bloat and latency spikes.
Understand ALiBi, the position encoding method that adds linear biases to attention scores, enabling exceptional length extrapolation without position embeddings.
Compare Multi-Head, Grouped-Query, and Multi-Query Attention mechanisms to understand their trade-offs and choose the optimal approach for your use case.
Understand attention sinks, the phenomenon where LLMs concentrate attention on initial tokens, and how preserving them enables infinite-length streaming inference.
Understand cross-attention, the mechanism that enables transformers to align and fuse information from different sources, sequences, or modalities.