
Transformers Unmasked: Beyond Attention Is All You Need
Transformer models have revolutionized the field of natural language processing (NLP) and are increasingly impacting other domains like computer vision and time series analysis. Their ability to process sequential data in parallel and capture long-range dependencies has led to breakthroughs in various applications, from machine translation to text generation. This blog post dives deep into the inner workings of transformer models, exploring their architecture, advantages, applications, and future trends.
Understanding Transformer Architecture
Transformer models, introduced in the groundbreaking paper "Attention is All You Need," moved away from recurrent neural networks (RNNs) and convolutional neural networks (CNNs) for sequence transduction. They rely entirely on attention mechanisms to ...