#self-attention

Attending to all attentions (a try!) (Part 1)

From Bahdanau’s original idea to FlashAttention and Multi-Head Latent Attention.

“Attention Is All You Need,” Finally Explained : Before You Build Another AI Agent, Understand This

If you’ve ever wondered how ChatGPT understands your questions, why AI can now write code, translate languages, and reason about complex…

Lost in the Middle: Why Your AI Keeps Forgetting and What Context Windows Really Do

Have you ever pasted a long document into an AI tool, asked a question about a detail in the centre… and the AI completely missed it?

AXIS: Self-Attention Unpacked

How a Transformer Lets a Word Understand Its Neighbors (or context around it)

Your attention is a gift. Most people don’t know how to give it.

Your attention is not a small thing. It is the signal that tells another person’s nervous system: you exist, you matter, you are not alone.

All you need is Attention

Understanding Attention: The Shift That Redefined NLP

Transformers: The Architecture That Teaches Machines to Read

Imagine trying to understand a complex novel, but you are only allowed to read one word at a time, and you must forget the previous…

Will it fit? Knowing your GPU VRAM before you press run

A back-of-the-napkin guide to estimating LLM memory — for inference and training — without loading a single weight.

Why ChatGPT Can Find Your Lost Document but Ctrl+F Can’t

A friendly introduction to embedding models. Series: Fine-Tuning Embedding Models for Domain-Specific Retrieval Part 1 of 4

“Attention Is All You Need”: One Architecture Quietly Changed the Future of Intelligence

I . Introduction

Fully Transparent Mini Transformer: Complete Numerical Walkthrough with Positional Encoding — The…

A Step-by-Step Educational Model on the Sentence “The animal didn’t cross the street because it was too tired.” (d_model=4)

How Language Models Learn the Way Humans Do?

The short answer to the question “How Language Models Learn the Way Humans Do?” is MULTI-HEAD ATTENTION.

Transformer Architecture : Core Concepts — Questions & Answers

Transformer Architecture is the backbone of modern AI and Large Language Models. In this second article of our AI Engineering Series, we…

Understanding Attention

From Embeddings to KV Cache: How Transformers Actually Work

The Real Question Isn’t Attention vs Memory. It’s What’s Coming After Both.

Part 9 of the Mamba series. The final piece. Everything until now has been about understanding specific architectures. This article is…

Self-Attention: The Mechanism That Rewired AI

Part 1 of the Attention series

Transformers and Mamba Aren’t Competing. They’re Doing Different Jobs.

Part 8 of the Mamba series. You now know both architectures deeply. This article is about when to reach for each one — and why the…

Transformer Models Part 1: Self-Attention & Multi-Head Attention

Feel free to read about RNNs here: https://www.linkedin.com/pulse/recurrent-neural-network-rnn-part-1-sonal-prasad-1kcjc

Mamba 3 Was Built Backwards — And That’s Why It’s Fast

Part 7 of the Mamba series. Previously: Mamba 3 restored state tracking via data-dependent RoPE. This article is about the other two…

Self-Attention and Multi-Head Attention in NLP

In the last blog, we learn about attention and why it became such an important idea in sequence modeling. It solved a real problem in…

# Demystifying Self-Attention

Attention Is All You Need

The 2017 paper that changed everything about how AI understands language.

What are Transformers?

You all might have heard of this word transformers going around ever since GPT came up. GPT stands for Generative Pre-trained Transformer…

Self-Attention Without Math (Explained Simply)

Self-attention is how a model decides which words are important to each other in a sentence.

Lookback (3) — The Transformer: Where Modern LLMs Began

Introduction — Why Attention Is All You Need Changed the Trajectory of AI

My birthday is only important to the EGO

Birthdays bring attention. Calls, messages, and wishes create a sense of importance for a single day. Yet the moment the day ends, the…

Is Self-Attention Overrated? Meet gMLP, the Simple Model That’s Challenging Transformers

In the world of artificial intelligence, the Transformer architecture is the undisputed heavyweight champion. Since its debut in 2017, it…

Universal Transformers- Iterative Refinement for Better Reasoning

This blog is based on the paper “Universal Transformers” (Dehghani et al., ICLR 2019) https://arxiv.org/pdf/1807.03819

Crafting a Self-Attention-Driven VAE with Hyperdimensional Computing and Neuro-Symbolic AI for…

Introduction

In this blog post, we will delve into the intricate details of the Transformer architecture…

Table of contents

Grouped-query attention이란 무엇인가?

최근 발표된 성능 좋은 오픈소스 모델은 대부분 Attention을 개선하여 성능을 개선시키고 있는 것 같습니다. 이번 포스트에서는 Meta의 Llama2와 Google의 gemma에서 사용된 GQA와 MQA에 대해서 알아보려고 합니다.

Rethinking SENet: Squeeze and Excitation Network

Looking Back to Look Forward

Lecture Notes for MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention

This lecture note will keep track of key concepts gone over in lecture 2 of the MIT 2023 deep learning course. For each concept, I will go…

Channel Attribution needs Attention!

Marketing attribution is a way of measuring the value of the campaigns and channels that are reaching your potential customers. The point…