[Day66] Transformer 배경 이해하기

오늘은 자연어처리 심화과정의 이론부분을 학습하는 날입니다. 트랜스포머에 대해서 배웠는데요. 트랜스포머를 이해하는데 도움이 되는 글들을 공유합니다.

1. Attn: Illustrated Attention

eda-ai-lab.tistory.com/157

Attn: Illustrated Attention

2. [Attention] Luong Attention 개념 정리

hcnoh.github.io/2019-01-01-luong-attention

[Attention] Luong Attention 개념 정리

3. Attention Is All You Need (paper)

arxiv.org/pdf/1706.03762.pdf

4. positional encoding이란 무엇인가

skyjwoo.tistory.com/31

positional encoding이란 무엇인가

5. Transformer: Attention is all you need

reniew.github.io/43/

Transformer: Attention is all you need

6. Attention is all you need paper 뽀개기

pozalabs.github.io/transformer/

Attention is all you need paper 뽀개기

7. Applying AutoML to Transformer Architectures

ai.googleblog.com/2019/06/applying-automl-to-transformer.html

Applying AutoML to Transformer Architectures

8. Introduction to Deep Learning Normalization

subinium.github.io/introduction-to-normalization/

Introduction to Deep Learning Normalization

9. ResNet 

itrepo.tistory.com/36

(7) ResNet (Residual Connection)

10. Transformer - Harder, Better, Faster, Stronger

blog.pingpong.us/transformer-review/

Transformer - Harder, Better, Faster, Stronger

11. Transformer-XL 자료정리 및 설명요약

comeng.tistory.com/entry/Transformer-XL-%EC%9E%90%EB%A3%8C%EC%A0%95%EB%A6%AC-%EB%B0%8F-%EC%84%A4%EB%AA%85%EC%9A%94%EC%95%BD

Transformer-XL 자료정리 및 설명요약

12. Reformer:효율적인 트랜스포머

brunch.co.kr/@synabreu/31

Reformer:효율적인 트랜스포머

13. 월간 자연어 처리 (페이스북)

www.facebook.com/monthly.nlp/posts/183595483072324?__xts__[0]=68.ARD9buXAhq6wKHnZB-Mjz540FT90HVC5ROhXM_l3rqPKG7DNjinZ59edHSY0m9CrRTHf8-LRFu0yx7-OHHiunKYWCibr9KleoiONVm8dCZfKy2CAmM-J200LJtN9s1rotSds2-nqPan-KQ5ovwW9u0YJ86Mv-eqbT85tsX9RRTavsXoQDEUkKGWnvkT5NdIDZa4DrlcXVoAxrOoBCYEPvHDooR4juviHA3C0lNP33Ls89dPbWShj07qTBi76YCydQo8YBLs2JOxBrR-N462esnRm8gp4Ijwx5BvbUcL1oMcvAfBI8o97ODBjPwmzm_UKyNp_y3T-1B4VyNtmsmc0edI

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