A complete end-to-end playlist on Deep Learning where topics like ANN, CNN, and RNN are covered.
Curated by: CampusX (84 videos)
Cross Attention is a mechanism in transformer models where the attention is applied between different sequences, typically between the output of one layer and the input of another. It allows the model to focus on relevant parts of the input sequence while generating output, making it crucial in tasks like translation, where the decoder attends to the encoder's output to generate accurate translations. Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes ============================ Did you like my teaching style? Check my affordable mentorship program at : https://learnwith.campusx.in DSMP FAQ: https://docs.google.com/document/d/1OsMe9jGHoZS67FH8TdIzcUaDWuu5RAbCbBKk2cNq6Dk/edit#heading=h.gvv0r2jo3vjw ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official Slide into our DMs: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at support@campusx.in ⌚Time Stamps⌚ 00:00 - Plan Of Action 03:11 - What is Cross attention 11:35 - The "HOW" of Cross attention 12:45 - Self Attention vs Cross Attention(Input) 15:02 - Self Attention vs Cross Attention (Processing) 24:18 - Self Attention vs Cross Attention (Output) 29:01 - Cross Attention vs Bahdanau/Luang Attention 32:00 - Use Cases