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We're ecstatic to bring you "How Transformer LLMs Work" -- a free course with ~90 minutes of video, code, and crisp visuals and animations that explain the modern Transformer architecture, tokenizers, embeddings, and mixture-of-expert models.
@MaartenGr and I have developed a lot of the visual language over the last several years (tens of thousands of iterations for hundreds of figures) for the book. But to have an opportunity to collaborate with the legendary @AndrewYNg, we took them to the next level with animations and a concise narrative meant to enable technical learners to pick up an ML paper and understand the architecture description.
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@MaartenGr and I have developed a lot of the visual language over the last several years (tens of thousands of iterations for hundreds of figures) for the book. But to have an opportunity to collaborate with the legendary @AndrewYNg, we took them to the next level with animations and a concise narrative meant to enable technical learners to pick up an ML paper and understand the architecture description.
Link in comments
Announcing How Transformer LLMs Work, created with @JayAlammar and @MaartenGr, co-authors of the beautifully illustrated book, “Hands-On Large Language Models.”
This course offers a deep dive into the inner workings of the transformer architecture that powers large language models (LLMs).
The transformer architecture revolutionized generative AI; in fact, the "GPT" in ChatGPT stands for "Generative Pre-Trained Transformer." Originally introduced in the Google Brain team's groundbreaking 2017 paper "Attention Is All You Need," by Vaswani and others, transformers were a highly scalable model for machine translation tasks. Variants of this architecture now power today’s LLMs such as those from OpenAI, Google, Meta, Cohere, Anthropic and DeepSeek.
In this course, you’ll learn in detail how LLMs process text. You'll also work through code examples that illustrate that transformer's individual components.
In details, you’ll learn:
- How the representation of language has evolved, from Bag-of-Words to Word2Vec embeddings to the transformer architecture that captures a word's meanings taking into account the context of other words in the input.
- How inputs are broken down into tokens before they are sent to the language model.
- The details of a transformer's main stages: Tokenization and embedding, the stack of transformer blocks, and the language model head.
- The inner workings of the transformer block, including attention, which calculates relevance scores, and the feedforward layer, which incorporates stored information learned in training.
- How cached calculations make transformers faster.
- Some of the most recent ideas in the latest models such as Mixture-of-Experts (MoE) which uses multiple sub-models and a router on each layer to improve the quality of LLMs.
By the end of this course, you’ll have a deep understanding of how LLMs actually process text and be able to read through papers describing the latest models and understand the details.
Gaining this intuition will improve your approach to building LLM applications.
Please sign up here: deeplearning.ai/short-courses/…View quoted post on X
