Attention Is All You Need: A Beginner-Friendly Guide to Transformers
Introduction In 2017, the paper “Attention Is All You Need” introduced a new neural network architecture called the Transformer that revolutionized natural language processing. Transformers moved away from the traditional recurrent neural networks (RNNs) and instead relied entirely on a mechanism called attention to handle sequences of data. This breakthrough enabled models that are faster to train, better at capturing long-range dependencies in text, and ultimately led to modern language models like BERT and GPT. In this post, we will build an intuitive understanding of the Transformer architecture. We’ll start by examining why earlier sequence models (like RNNs) struggled, then explain attention mechanisms (especially self-attention and multi-head attention), and finally walk through the Transformer’s encoder-decoder architecture including positional encoding. By the end, you should understand how and why Transformers work so well – in clear, beginner-friendly terms. ...