Introduction to Transformers for LLMs Course Now Available!

It is almost Christmas, so I am glad to be able to release my latest course: Introduction to Transformers for LLMs. To celebrate that, the first 1000 students redeeming the following coupon will get access to the course for only $5!

• Course page: Introduction to Transformers for LLMs

• Coupon: FIVE

This course is going to be the first of a series of courses on LLMs. This course will give you the foundations to understand how the Transformer architecture is used for Large Language Models. Here are the topics covered in that course:

• The RNN Encoder-Decoder Architecture

• The Attention Mechanism Before Transformers

• The Self-Attention Mechanism

• Understanding the Transformer Architecture

• How do we create Tokens from Words

• How LLMs Generate Text

• Transformers' applications beyond LLMs

This course is for Machine Learning enthusiasts who want to understand the inner workings of Transformer architecture. We are going to explore the different models that led to that discovery back in 2017. From the RNN Encoder-Decoder architecture, passing by the Bahdanau and Luong Attention mechanisms, up to the self-attention mechanism. We are going to dive into the strategy to parse text into tokens before feeding them to the LLMs and how LLMs can be tuned to generate text.

Each section will divided into the conceptual part and the coding part. I recommend digging into both aspects, but feel free to focus on the concepts or the coding if it matters more to you. I made sure to separate the two for learning flexibility. In the coding part, we are going to see how the different models are implemented in PyTorch, and we are going to explore some of the capabilities of the Transformers Python package by Hugging Face. However, this is not a PyTorch course, and I will not dive into the details of the framework.

Let me know if you have any feedback!