This week we’re looking into transformers. Transformers were introduced a couple of years ago with the paper Attention is All You Need by Google Researchers. Since its introduction transformers has been widely adopted in the industry.

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Models like BERT, GPT-3 made groundbreaking improvements in the world of NLP using transformers. Since then model libraries like hugging face made it possible for everyone to use transformer based models in their projects. But what are transformers and how do they work? How are they different from other deep learning models like RNNs, LSTMs? Why are they better?

In this video, we learn about it all!

Some of my favorite resources on Transformers:
The original paper - https://arxiv.org/pdf/1706.03762.pdf
If you’re interested in following the original paper with the code - http://nlp.seas.harvard.edu/2018/04/0...
The Illustrated Transformer – https://jalammar.github.io/illustrate...
Blog about positional encodings - https://kazemnejad.com/blog/transform...
About attention - Visualizing A Neural Machine Translation Model - https://jalammar.github.io/visualizin...
Layer normalization - https://arxiv.org/abs/1607.06450


Some images used in this video are from:
https://colah.github.io/posts/2015-08...
https://jalammar.github.io/visualizin...
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