Step-by-Step Exploration of Transformer Attention Mechanisms

Author(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 Dynamics

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If you’re 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, you’ll see why they’re so powerful and versatile.

Imagine you’re 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. That’s 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 it’s 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. It’s like having a spotlight that focuses on the most important words in a sentence while still keeping an eye on the rest.

We’re going to break it all down… Read the full blog for free on Medium.

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Published via Towards AI