Evolution of the Transformer Architecture Used in LLMs (2017–2025) – Full Course
1,086This 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:
• Train Your Own LLM – Tutorial
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 learhttps://scrimba.com/freecodecampdecamp
🎉 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 developerhttps://www.freecodecamp.org/mp.org
Read hundreds of articles on programhttps://freecodecamp.org/newsg/news