A Gentle Introduction to Attention and Transformer Models
This post is divided into three parts; they are: • Origination of the Transformer Model • The Transformer Architecture • Variations of the Transformer Architecture Transformer architecture originated from the 2017 paper “Attention is All You Need” by Vaswani et al.
This article is divided into three parts; they are: • Full Transformer Models: Encoder-Decoder Architecture • Encoder-Only Models • Decoder-Only Models The original transformer architecture, introduced in "Attention is All You Need," combines an encoder and decoder specifically designed for sequence-to-sequence (seq2seq) tasks like machine translation.
This post covers three main areas: • Why Mixture of Experts is Needed in Transformers • How Mixture of Experts Works • Implementation of MoE in Transformer Models The Mixture of Experts (MoE) concept was first introduced in 1991 by
This post is divided into six parts; they are: • Why Transformer is Better than Seq2Seq • Data Preparation and Tokenization • Design of a Transformer Model • Building the Transformer Model • Causal Mask and Padding Mask • Training and Evaluation Traditional seq2seq models with recurrent neural networks have…