Transformer Architecture Explained from Scratch with Detailed Math Examples

Watch now and learn the mathematics behind the Transformer Architecture! 🔗
In this video, we're going to dive into the Transformer Architecture and explore its various components, including token embeddings, positional encoding, attention, and feed-forward blocks. You'll learn about the encoder-decoder architecture and how it's used in sequence-to-sequence tasks. We'll also cover the importance of multi-head attention and how it's used in the Transformer model.
Contents Covered
Introduction to the Transformer Architecture
Token Embeddings and Positional Encoding
Attention Mechanism and Multi-Head Attention and its Variants
Feed-Forward Blocks and Layer Normalization
Training and Inference Process
Decoding Strategy and Greedy Decoding
Key Takeaways:
Learn the Transformer Architecture and its various components
Understand the importance of multi-head attention and how it's used in the model
Discover how the feed-forward blocks and layer normalization work
Understand the training and inference process
Learn about different decoding strategies and greedy decoding
What's Next: In the next video, we'll implement the Transformer Architecture for translation tasks. Stay tuned!
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[@agamergen]

Please keep going. I love your creativity

[@SowmyaRao-d9g]

Very detailed and you explained it very well.

[@openai.deepaksingh]

This was awesome. I cleared my lot many doubts. Hope this channel keeps bringing such videos.

[@agamergen]

As always you chew it very well so we can swallow it easily ❤

[@prashlovessamosa]

Thanks Anna

[@ShivamPradhan-c1x]

can you add the resource link of this video

[@venkatagangadharraoy5407]

Thanks for the video. Can you please share the resource as well

[@NavdeepVarshney-ep4ck]

Can I get your number are u a researcher