We break down the Encoder architecture in Transformers, layer by layer! If you've ever wondered how models like BERT and GPT process text, this is your ultimate guide.

We look at the entire design of Encoder architecture in Transformers and why its implemented that way.

What You’ll Learn:
Word Embeddings & Their Limitations – Why static embeddings fail and how we make them context-aware
Self-Attention Explained – How words influence each other dynamically
Multi-Headed Attention – Why multiple attention heads are necessary for understanding complex relationships
Positional Encoding – How Transformers handle word order despite parallel processing
Add & Norm Layer – The role of residual connections and normalization
Feed-Forward Network – Why we need non-linearity and how it enhances model capacity

By the end of this video, you'll have a crystal-clear understanding of the Encoder Architecture in Transformers and how it processes text efficiently.

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Timestamps:
0:00 Intro
0:42 Input Embeddings
2:29 Self Attention
3:45 Multi-headed Attention
7:49 Positional Encodings
10:07 Add & Norm Layer
13:15 Feed Forward Network
19:47 Stacking Encoders
22:28 Outro



Follow my entire Transformers playlist :

Transformers Playlist: https://www.youtube.com/watch?v=lRylkiFdUdk&list=PLuhqtP7jdD8CQTxwVsuiFYGvHtFpNhlR3&index=1&t=0s



RNN Playlist: https://www.youtube.com/watch?v=lWPkNkShNbo&list=PLuhqtP7jdD8ARBnzj8SZwNFhwWT89fAFr&t=0s

CNN Playlist: https://www.youtube.com/watch?v=E5Z7FQp7AQQ&list=PLuhqtP7jdD8CD6rOWy20INGM44kULvrHu&t=0s

Complete Neural Network: https://www.youtube.com/watch?v=mlk0rddP3L4&list=PLuhqtP7jdD8CftMk831qdE8BlIteSaNzD&t=0s

Complete Logistic Regression Playlist: https://www.youtube.com/watch?v=U1omz0B9FTw&list=PLuhqtP7jdD8Chy7QIo5U0zzKP8-emLdny&t=0s

Complete Linear Regression Playlist: https://www.youtube.com/watch?v=nwD5U2WxTdk&list=PLuhqtP7jdD8AFocJuxC6_Zz0HepAWL9cF&t=0s