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attention-transformers

Attention transformers are a groundbreaking architecture in machine learning, primarily designed for natural language processing (NLP) tasks. Introduced in the seminal paper “Attention Is All You Need” by Vaswani et al. in 2017, these models utilize a self-attention mechanism that allows them to weigh the significance of different words in a sentence. This capability enables transformers to excel in various applications, including translation, summarization, and question-answering. Unlike traditional recurrent neural networks (RNNs), transformers can process entire sequences simultaneously, leading to faster training and improved performance across a wide range of tasks in AI and data science.

Transformers (Attention Is All You Need) In Depth

Python in Plain English

Transformers, in the context of machine learning and artificial intelligence, refer to a type of deep learning model architecture designed primarily for natural language processing (NLP) tasks. They h...

Transformers in Action: Attention Is All You Need

Towards Data Science

Transformers A brief survey, illustration, and implementation Fig. 1. AI-generated artwork. Prompt: Street View Of A Home In The Style Of Storybook Cottage. Photo generated by Stable diffusion. Link ...

Transformers: Attention is all You Need

Python in Plain English

Introduction In one of the previous blogs, we discussed LSTMs and their structures. However, they are slow and need the inputs to be passed sequentially. Because today’s GPUs are designed for paralle...

Understanding Attention In Transformers

Towards AI

An intuitive introduction and theoretical reasoning for how and why Transformers are so damn effective and essentially consuming the whole machine learning world. Introduction Transformers are everyw...

Attention and Transformer Models

Towards Data Science

“Attention Is All You Need” by Vaswani et al., 2017 was a landmark paper that proposed a completely new type of model — the Transformer. Nowadays, the Transformer model is ubiquitous in the realms of…...

Building Blocks of Transformers: Attention

Towards AI

The Borrower, the Lender, and the Transformer: A Simple Look at Attention It’s been 5 years…and the Transformer architecture seems almost untouchable. During all this time, there was no significant c...

Explaining Attention in Transformers [From The Encoder Point of View]

Towards AI

Photo by Devin Avery on Unsplash In this article, we will take a deep dive into the concept of attention in Transformer networks, particularly from the encoder’s perspective. We will cover the followi...

Attention for Vision Transformers, Explained

Towards Data Science

Vision Transformers Explained Series The Math and the Code Behind Attention Layers in Computer Vision Since their introduction in 2017 with Attention is All You Need¹, transformers have established t...

A Deep Dive into the Self-Attention Mechanism of Transformers

Analytics Vidhya

Introduction: In recent years, large language models (LLMs) have revolutionized the field of Natural Language Processing (NLP). These models, capable of generating human-like text, translating langua...

The Transformer Attention Mechanism

Machine Learning Mastery

Last Updated on October 23, 2022 Before the introduction of the Transformer model, the use of attention for neural machine translation was implemented by RNN-based encoder-decoder architectures. The T...

The Transformer: Attention Is All You Need

Towards Data Science

The Transformer paper, “Attention is All You Need” is the 1 all-time paper on Arxiv Sanity Preserver as of this writing (Aug 14, 2019). This paper showed that using attention mechanisms alone, it’s…

The Math Behind Multi-Head Attention in Transformers

Towards Data Science

Deep Dive into Multi-Head Attention, the secret element in Transformers and LLMs. Let’s explore its math, and build it from scratch in Python Image generated by DALL-E 1: Introduction 1.1: Transforme...