PyTorch Practical - Multihead Attention Computation in PyTorch

In this tutorial, you will learn how to how perform multihead attention computation in PyTorch. Multihead attention is the block in the Transformer model responsible for taking the input embeddings and enriching it using attention information based on the keys, queries and values.

The Keys, Queries and Values are obtained by taking the dot product of the embeddings matrix with the learnt weight matrices of the model

Other Tutorials on Transformer Architecture
- Attention Mechanism in Transformers - https://youtu.be/O60ycpH72C0
- Self-Attention vs Cross-Attention - https://youtu.be/BxocebEC03E
- Linear Transformation of Embeddings to Queries, Keys and Values - https://youtu.be/Vr4d69t_EZc
- Understanding Scaled Dot Product - https://youtu.be/gVGXRuzJ7d8
- PyTorch Practical - How to Compute Scaled Dot Product Attention
- The Decoder Block of the Transformer model - https://youtu.be/oldZQUCWm9Y

You can reach me via any of the following
❤️ Instagram: https://www.instagram.com/kindsonthegius
❤️ LinkedIn: https://www.linkedin.com/in/kindson/
❤️ Pinerest: https://www.pinterest.com/kindsonm/
❤️ Facebook: https://www.facebook.com/kindsonm/
❤️ Tumblr: https://www.tumblr.com/blog/kindsonthegenius
❤️ Twitter: https://twitter.com/KindsonM

🙏🙏🙏 Your support can help me improve my content:
✅ Click on the Thanks button below the video
✅ Buy me a coffee: https://www.buymeacoffee.com/KindsonM
✅ Support me on Patreon: https://www.patreon.com/kindsonthegenius

Видео PyTorch Practical - Multihead Attention Computation in PyTorch канала Kindson The Tech Pro