Attention Is All You Need Explained: Part 1 — Introduction & Transformer Architecture

Alex and Thuy dive into the groundbreaking 2017 paper that introduced the Transformer architecture, revolutionizing NLP and becoming the foundation for GPT, BERT, and modern large language models.

In this episode:

• Why RNNs and CNNs were replaced — The limitations of sequential processing
• Encoder-decoder architecture — How the Transformer processes input and generates output
• Multi-head self-attention — The key innovation that makes it all work
• Scaled dot-product attention — The query-key-value framework explained
• Positional encoding — Using sine and cosine functions to encode word order
• Residual connections and layer normalization — Training stability tricks
• Parallel processing — How this makes training dramatically faster than RNNs

Paper

• Title: Attention Is All You Need
• Authors: Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin (Google Brain / Google Research / University of Toronto)
• Published: June 2017 (NeurIPS 2017)
• Link: arxiv.org/abs/1706.03762

Series

This is Part 1 of a 3-part series on the original Transformer paper.

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Hosted by Alex (PM at a fintech scale-up) and Thuy (AI researcher)