Transformers have revolutionized deep learning, but have you ever wondered how the decoder in a transformer actually works? In this video, we break down Decoder Architecture in Transformers step by step!

What You’ll Learn:
The fundamentals of encoding-decoding in deep learning and how it's different in Transformers.
The role of each layer in the decoder and how they work together.
A deep dive into masked self-attention, cross-attention, and feed-forward networks in the decoder.
How transformers generate meaningful sequences in tasks like language modeling, machine translation, and text generation.

By the end of this video, you'll have be able to map the entire Decoder Architecture in Transformers by hand, using a pen & a paper and understand why it's so powerful!

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Timestamps:
0:00 Intro
0:56 Encoder-Decoder model in Deep Learning
2:24 Encoder-Decoder in Transformers
5:25 Parallelizing Training in Transformers
12:57 Masked Multi-head attention
19:29 Encoder-Decoder in training of Transformers
22:01 Positional Encodings
23:08 Add & Norm Layer
24:47 Cross Attention
32:33 Feed Forward Network
33:53 Stacking of Decoder blocks
34:42 Final Prediction Layer
37:06 Decoder during inference
40:05 Outro



Check my Encoder Architecture video: https://www.youtube.com/watch?v=BIMV3Av5CRU&t=744s



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