Learn the evolution of transformer architecture used in LLM

by SkillAiNest

Transformers have changed the game in machine learning. From chat boats and search engines to strengthen machine translation and image generation, they are the real status of today’s most impressive AI models. But the field moves forward. New techniques and dissent are permanently improving how transformers perform. If you want to maintain, it is important to understand these changes.

We have just published a new course on the Free Codecamp.com on the YouTube channel that breaks the latest reforms in transformer architecture. It is initially friendly, there is no fruit, and you move through every concept of step by step. Whether you are all new to learning deep or already familiar with transformers and want to understand how they are ready, this course will bring you a speedy speed.

Will you learn

Developed by Emad Sadak, this course covers modern ideas and dissent that make modern transformers fast, more accurate and more expanded. It focuses on explaining and simplicity so that you can really understand “why” behind every change, not just “what”.

You’ll learn about:

  • Local encoding techniques (Why do they make a difference and how to improve them)

  • Different mechanisms of attention And when to use them

  • Normalize

  • Activation functions Which are common in modern transformers

  • And different types of other small renovations that collectively make a big difference

The structure of the course

What has been covered here in each section:

  1. Overview of the course – What is the structure of the course to be made

  2. Introduction – a quick refresher on primary transformer components

  3. Positional encoding – Understand why it makes a difference and how it is getting ready

  4. Focus procedures -Discover variations beyond standard self -esteem

  5. Deserted – Dive into adaptation that improves performance and performance

  6. Keeping everything together – see how all the pieces work in context

  7. Conclusion – Final ideas and where to go from here

See now

This course is ideal for this:

  • Students and engineers are just starting with transformers

  • Anyone who learned the original transformer model and wants to catch improvement

  • Practitioners who want a clear understanding of opportunities used in GPT, British variations and beyond models

You do not need a deep knowledge of mathematics from the beginning or the construction model of a pre -experience. Only a basic understanding of how the transformers work will help you walk with you.

You can see the full course in free Freecodecamp.org YouTube channel (3 hours clock)

https://www.youtube.com/watch?v=8wbs0dt0h2i

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