By the end of this lesson, you will understand the core mechanism of Transformer models, specifically self-attention, how it enables parallel processing of language, and why this architecture surpassed previous recurrent neural networks in AI applications like ChatGPT.
The secret to modern AI's language prowess isn't complex sequential processing, but a mechanism that lets it 'look' at all parts of a sentence simultaneously.
Before Transformers, models like Recurrent Neural Networks (RNNs) processed words one by one, struggling with long-range dependencies.