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
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