Transformers explained | The architecture behind LLMs

All you need to know about the transformer architecture: How to structure the inputs, attention (Queries, Keys, Values), positional embeddings, residual connections. Bonus: an overview of the difference between Recurrent Neural Networks (RNNs) and transformers.
9:19 Order of multiplication should be the opposite: x1(vector) * Wq(matrix) = q1(vector). Otherwise we do not get the 1x3 dimensionality at the end. Sorry for messing up the animation!

Check this out for a super cool transformer visualisation! 👏 https://poloclub.github.io/transforme...

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Outline:
00:00 Transformers explained
00:47 Text inputs
02:29 Image inputs
03:57 Next word prediction / Classification
06:08 The transformer layer: 1. MLP sublayer
06:47 2. Attention explained
07:57 Attention vs. self-attention
08:35 Queries, Keys, Values
09:19 Order of multiplication should be the opposite: x1(vector) * Wq(matrix) = q1(vector).
11:26 Multi-head attention
13:04 Attention scales quadratically
13:53 Positional embeddings
15:11 Residual connections and Normalization Layers
17:09 Masked Language Modelling
17:59 Difference to RNNs

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Dres. Trost GbR, Siltax, Vignesh Valliappan, ‪@Mutual_Information‬ , Kshitij

Our old Transformer explained 📺 video:    • The Transformer neural network architectur...  
📺 Tokenization explained:    • What is tokenization and how does it work?...  
📺 Word embeddings:    • How modern search engines work – Vector da...  
📽️ Replacing Self-Attention:    • Replacing Self-attention  
📽️ Position embeddings:    • Position encodings in Transformers explain...  
‪@SerranoAcademy‬ Transformer series:    • The Attention Mechanism in Large Language ...  

📄 Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. "Attention is all you need." Advances in neural information processing systems 30 (2017).

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