Evolution of the Transformer Architecture Used in LLMs (2017–2025) – Full Course

This course introduces the latest advancements that have enhanced the accuracy, efficiency, and scalability of Transformers. It is tailored for beginners and follows a step-by-step teaching approach. In this course, you’ll explore: - Various techniques for encoding positional information - Different types of attention mechanisms - Normalization methods and their optimal placement - Commonly used activation functions - And much more You can find the slides, notebook, and scripts in this GitHub repository: https://github.com/ImadSaddik/Train_Your_Language_Model_Course Watch the previous course on LLMs mentioned in the introduction: https://www.youtube.com/watch?v=9Ge0sMm65jo To connect with Imad Saddik, check out his social accounts: YouTube: @3CodeCampers LinkedIn: /imadsaddik Discord: imad_saddik ⭐️ Course Contents ⭐️ (0:00:00) Course Overview (0:03:24) Introduction (0:05:13) Positional Encoding (1:02:23) Attention Mechanisms (2:18:04) Small Refinements (2:42:19) Putting Everything Together (2:47:47) Conclusion ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp 🎉 Thanks to our Champion and Sponsor supporters: 👾 Drake Milly 👾 Ulises Moralez 👾 Goddard Tan 👾 David MG 👾 Matthew Springman 👾 Claudio 👾 Oscar R. 👾 jedi-or-sith 👾 Nattira Maneerat 👾 Justin Hual -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news