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THE VIDEO SHELF · transformers in nlp

LSTM is dead. Long Live Transformers!

Leo Dirac explains how transformer models have replaced LSTMs in NLP. He gives background from bag of words through RNNs and LSTMs, then a technical deep dive into multi-headed self-attention and positional encoding, with sample code.

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Why HN readers recommended it

Recommended as a clear explanation of why RNNs fall short and how transformers solve it.

This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.

Video & recommendation details

Creator / channel
Seattle Applied Deep Learning
Topic
transformers in nlp
Format
lecture
Level
intermediate
HN recommenders in dataset
4
HN mentions in dataset
5
Recommendation years in dataset
2020–2023

These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.

From the original HN conversations

Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.

  • I don't think you really want a 5yo's version, because that's "computers can be smart". But for an SDE1-level explanation I made this video which has gotten tons of compliments and keeps people watching year after year: [VIDEO] It's in the context of NLP, which …

    leopd on Hacker News · 2023-05
    In Ask HN: Can someone ELI5 Transformers and the “Attention is all we need” paper

  • I've just recently been on a journey to understand transformers in and out, here are the resources I've found managed to drill it into my head: 1. Chapters 7, 9, 10 [LINK] This was really useful to really build up to the concepts of attention …

    FL33TW00D on Hacker News · 2020-10
    In Rethinking Attention with Performers

  • Yannic Kilcher is great but this video worked better for me: "LSTM is dead. Long live transformers!" (Leo Dirac): [VIDEO]

    peterfirefly on Hacker News · 2023-01
    In The Transformer Family

Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.