100 Days of Deep Learning - Transformer Architecture | Part 1 Encoder Architecture | CampusX

The Encoder in transformer architecture processes input sequences by applying layers of multi-head self-attention and feed-forward networks. Each layer consists of self-attention mechanisms followed by layer normalization and feed-forward neural networks. This architecture enables the model to capture complex patterns and relationships in the input data, facilitating tasks like language translation and text summarization. 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] ✨ Hashtags✨ #campusx #deeplearning #transformers ⌚Time Stamps⌚ 00:00 - Intro 02:36 - Recap/Prerequisite 05:10 - Understanding Architecture 13:02 - Encoder Architecture 28:50 - Encoder - Feed Forward Network 41:39 - Some Questions 54:45 - Outro