Learn the Evolution of the Transformer Architecture Used in LLMs

Transformers have changed the game in machine learning. From powering chatbots and search engines to enabling machine translation and image generation, they're at the core of today’s most impressive AI models. But the field moves fast. New techniques...

Jun 27, 2025 - 03:20

Learn the Evolution of the Transformer Architecture Used in LLMs

Transformers have changed the game in machine learning. From powering chatbots and search engines to enabling machine translation and image generation, they're at the core of today’s most impressive AI models. But the field moves fast. New techniques and refinements are constantly improving how Transformers perform. Understanding these changes is key if you want to keep up.

We just published a new course on the freeCodeCamp.org YouTube channel that breaks down the latest improvements in Transformer architecture. It’s beginner-friendly, no fluff, and walks you through each concept step by step. Whether you're brand new to deep learning or already familiar with Transformers and want to understand how they’ve evolved, this course will get you up to speed.

What You’ll Learn

Created by Imad Saddik, this course covers the newer ideas and refinements that make modern Transformers faster, more accurate, and more scalable. It focuses on clarity and simplicity so you can really grasp the “why” behind each change, not just the “what.”

You’ll learn about:

Positional encoding techniques (why they matter and how they’ve improved)
Different attention mechanisms and when to use them
Normalization (LayerNorm, RMSNorm, and how placement affects performance)
Activation functions that are common in modern Transformers
And a variety of other small refinements that collectively make a big difference

Course Structure

Here’s what’s covered in each section:

Course Overview – What to expect and how the course is structured
Introduction – A quick refresher on basic Transformer components
Positional Encoding – Understand why it matters and how it’s evolving
Attention Mechanisms – Explore variations beyond the standard self-attention
Small Refinements – Dive into tweaks that improve performance and efficiency
Putting Everything Together – See how all the pieces work in context
Conclusion – Final thoughts and where to go from here

Watch now

This course is ideal for:

Students and engineers just getting started with Transformers
Anyone who learned the original Transformer model and wants to catch up on the improvements
Practitioners who want a clearer understanding of the tweaks used in models like GPT, BERT variants, and beyond

You don’t need deep math knowledge or prior experience building models from scratch. Just a basic understanding of how Transformers work will help you follow along.

You can watch the full course for free on the freeCodeCamp.org YouTube channel (3-hour watch).