100 Days of Deep Learning - Transformer Decoder Architecture | Deep Learning | CampusX

The Decoder in a transformer architecture generates output sequences by attending to both the previous tokens (via masked self-attention) and the encoder’s output (via cross-attention). Each decoder layer consists of multi-head self-attention, cross-attention, and feed-forward layers. This structure allows the model to generate coherent sequences by considering both past outputs and relevant input context, making it effective for tasks like text generation and translation. 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 [email protected] ⌚Time Stamps⌚ 00:00 - Plan of Attack 02:22 - Simplified View 10:10 - Deep Dive into Architecture