Demystifying Attention: Building It from the Ground Up 0 like May 10, 2025 Share this post Author(s): Marcello Politi Originally published on Towards AI. A gentle dive into how attention helps neural networks remember better and forget lessPhoto by Codioful (Formerly Gradienta) on Unsplash The Attention Mechanism is often associated with the transformer architecture, but it was already used in RNNs. In Machine Translation or MT (e.g., English-Italian) tasks, when you want to predict the next Italian word, you need your model to focus, or pay attention, on the most important English words that are useful to make a good translation. Image from https://medium.com/swlh/a-simple-overview-of-rnn-lstm-and-attention-mechanism-9e844763d07b I will not go into details of RNNs, but attention helped these models to mitigate the vanishing gradient problem and to capture more long-range dependencies among words. At a certain point, we understood that the only important thing was the attention mechanism, and the entire RNN architecture was overkill. Hence, Attention is All You Need! Classical attention indicates where words in the output sequence should focus attention in relation to the words in the input sequence. This is important in sequence-to-sequence tasks like MT. The self-attention is a specific type of attention. It operates between any two elements in the same sequence. It provides information on how “correlated” the words are in the same sentence. For a given token (or word) in a sequence, self-attention generates a list of attention weights corresponding to all other tokens in the sequence. This… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI Towards AI - Medium Share this post