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Google Brain - Google Research | Presentation by MAACHOU Maroua
Attention is all you
need
TABLE OF
CONTENTS
Background
General Idea
01
02
03
04
05
Encoder
Decoder
Encoder - Decoder Attention
mechanism
More Details
Self - Attention explained
Background
01
Seq2Seq models - RNN
Problems in usual Seq2Seq model
→ Difficulty to summarize long-term dependencies
→ The need of a contextual embedding for the elements
of the sequence
→ Slow and difficult models to train
A General Idea
02
Transformer Model
Encoder
03
Self-Attention Explained
Architecture
Self-Attention
Decoder
04
Decoding Attention Mechanism
General Architecture
The Attention Mechanism
More details
05
Multi-Headed Attention
Positional Encoding
CREDITS: This presentation template was created by Slidesgo, including
icons by Flaticon, and infographics & images by Freepik.
Please keep this slide for attribution.
Does anyone have any questions?
marouamaachou@gmail.com
THANKS
■ The Paper : https://arxiv.org/pdf/1706.03762.pdf
■ The Illustrated Transformer : http://jalammar.github.io/illustrated-transformer/
■ The Annotated Transformer : https://nlp.seas.harvard.edu/2018/04/03/attention.html
■ Stanford NLP course :
https://www.youtube.com/watch?v=XXtpJxZBa2c&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY4
2z&index=8
■ Attention and Augmented Recurrent Neural Networks : https://distill.pub/2016/augmented-rnns/
■ Other Attention Mechanisms :
https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html#a-family-of-attention-mecha
nisms
■ My Implementation : https://github.com/marouamaachou
RESOURCES

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