Beyond Softmax — Sparse, Kernel & Linear Attention | LLM Math

Zariga Tongy · Intermediate ·🧠 Large Language Models ·3mo ago

About this lesson

Softmax attention mixes over all positions. Alternatives: sparse top-k weights, kernel views of similarity, and linear attention ideas that restructure the algebra for long sequences. Prereq: attention & softmax (5.4). 🔗 https://8gwifi.org/math #attention #softmax #linear attention #LLM #transformers #AI

Original Description

Softmax attention mixes over all positions. Alternatives: sparse top-k weights, kernel views of similarity, and linear attention ideas that restructure the algebra for long sequences. Prereq: attention & softmax (5.4). 🔗 https://8gwifi.org/math #attention #softmax #linear attention #LLM #transformers #AI

Watch on YouTube ↗ (saves to browser)

Sign in to unlock AI tutor explanation · ⚡30

Related Reads

We Built AI Using Wikipedia, But Wikipedia Is 40% Wrong

Learn how AI built using Wikipedia can be flawed due to the platform's inaccuracies and why this matters for AI development

Medium · Machine Learning

LLM vs RAG Explained (EP2): How AI Actually Finds the Right Answers

Learn how LLMs and RAGs work together to help AI find the right answers, and why they sometimes get things wrong

Medium · Data Science

LLM vs RAG Explained (EP2): How AI Actually Finds the Right Answers

Learn how LLMs and RAGs work together to help AI find the right answers, and why they sometimes get things wrong

LLM vs RAG Explained (EP2): How AI Actually Finds the Right Answers

Learn how LLMs and RAGs work to find the right answers, and why even smart AI models can make mistakes

5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems

Dave Ebbelaar (LLM Eng)