LatestMachine LearningStep-by-Step Exploration of Transformer Attention Mechanisms 0 like December 23, 2024Share this postLast Updated on December 24, 2024 by Editorial TeamAuthor(s): Shenggang Li Originally published on Towards AI. A Practical Walkthrough of Training Transformer Models with Insights into Positional Encoding and Its Role in Attention DynamicsThis member-only story is on us. Upgrade to access all of Medium.Photo by Abiyyu Zahy on UnsplashIf youre diving into AI and want to understand the secret sauce behind modern language models like ChatGPT or BERT, you need to get familiar with Transformers and their game-changing attention mechanism. These concepts are the foundation of cutting-edge NLP, and once you grasp them, youll see why theyre so powerful and versatile.Imagine youre trying to read a book, not line by line, but by flipping to any page you want instantly and picking up on the connections between parts of the story. Thats kind of what Transformers do in NLP. They ditched the old ways of reading word-by-word, like RNNs or LSTMs, and instead take in whole chunks of data whether its a sentence, a paragraph, or an entire sequence all at once. This gives them super speed in training and makes them great at spotting patterns across the whole text.At the heart of this magic is something called the attention mechanism. Its like having a spotlight that focuses on the most important words in a sentence while still keeping an eye on the rest.Were going to break it all down 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 asponsor. Published via Towards AITowards AI - Medium Share this post