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. Digital Notes for Deep Learning: https://shorturl.at/NGtXg ============================ 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