THE VIDEO SHELF · transformer architecture
Attention Is All You Need
Yannic Kilcher walks through the Attention Is All You Need paper, which introduced the Transformer, a sequence model based solely on attention without recurrence or convolutions. He reviews the architecture and the paper's translation results.
Why HN readers recommended it
HN recommends it as an accessible, clear walkthrough of the transformer paper for people with some AI background.
This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.
Video & recommendation details
- Creator / channel
- Yannic Kilcher
- Topic
- transformer architecture
- Format
- explainer
- Level
- intermediate
- HN recommenders in dataset
- 4
- HN mentions in dataset
- 5
- Recommendation years in dataset
- 2020–2025
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.
YouTube channel that explains the paper in detail: [VIDEO] And subsequent follow-ups (ROME, editing transformer arch): [LINK] I find the channel amazing explaining super complex topics in simple enough terms for people who have some background in AI.
bfung on Hacker News · 2023-01
In The Transformer FamilyEveryone has a different way of approaching. I like video walk-throughs of papers. If you are the same, for the theory bit you may find Yannic Kilcher's videos [0,1] helpful. On implementation side, the best project out there is [2] with good documentation. Also spacy …
kanche on Hacker News · 2020-10
In Rethinking Attention with PerformersFor accessibility I recommend Yannic Kilcher video review of "Attention Is All You Need" [VIDEO] Yannic has been making about 62 other transformer paper reviews since. You can find the usual suspects. [LINK]
visarga on Hacker News · 2021-12
In The potential of transformers in reinforcement learning
Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.