Скачать или смотреть Transformer Revolution: How "Attention Is All You Need" Changed AI Forever

In this episode, we cover the groundbreaking 2017 paper "Attention Is All You Need" by Vaswani et al., which introduced the Transformer architecture and revolutionized natural language processing.

The hosts break down the paper's key concepts including:
How the Transformer architecture works by replacing recurrent neural networks with self-attention mechanisms
The multi-head attention mechanism that allows parallel processing of sequences
Why Transformers dramatically reduced training time while achieving state-of-the-art results
How this architecture became the foundation for models like GPT, BERT, and modern AI chatbots
The paper's remarkable performance in machine translation and constituency parsing tasks

Source: Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017, June 12). Attention is all you need. arXiv.org. https://arxiv.org/abs/1706.03762

00:00:00 - Opening
00:00:05 - Intro
00:02:09 - Transformer Architecture Explained
00:10:22 - Transformer Translation Results
00:17:41 - Transformer Parsing Capabilities
00:23:27 - Closing