RoPE (Rotary positional embeddings) explained: The positional workhorse of modern LLMs
11:17
Rotary Positional Embeddings: Combining Absolute and Relative
13:39
How Rotary Position Embedding Supercharges Modern LLMs
33:50
Do we need Attention? A Mamba Primer
36:16
The math behind Attention: Keys, Queries, and Values matrices
23:26
Rotary Position Embedding explained deeply (w/ code)
1:10:55
LLaMA explained: KV-Cache, Rotary Positional Embedding, RMS Norm, Grouped Query Attention, SwiGLU
26:10
Attention in transformers, visually explained | DL6
27:14